Operationalizing the net-negative carbon economy.

The remaining carbon budget for limiting global warming to 1.5 degrees Celsius will probably be exhausted within this decade1,2. Carbon debt3 generated thereafter will need to be compensated by net-negative emissions4. However, economic policy instruments to guarantee potentially very costly net carbon dioxide removal (CDR) have not yet been devised. Here we propose intertemporal instruments to provide the basis for widely applied carbon taxes and emission trading systems to finance a net-negative carbon economy5. We investigate an idealized market approach to incentivize the repayment of previously accrued carbon debt by establishing the responsibility of emitters for the net removal of carbon dioxide through 'carbon removal obligations' (CROs). Inherent risks, such as the risk of default by carbon debtors, are addressed by pricing atmospheric CO2 storage through interest on carbon debt. In contrast to the prevailing literature on emission pathways, we find that interest payments for CROs induce substantially more-ambitious near-term decarbonization that is complemented by earlier and less-aggressive deployment of CDR. We conclude that CROs will need to become an integral part of the global climate policy mix if we are to ensure the viability of ambitious climate targets and an equitable distribution of mitigation efforts across generations.

Managing nitrogen to restore water quality in China.

The nitrogen cycle has been radically changed by human activities1. China consumes nearly one third of the world's nitrogen fertilizers. The excessive application of fertilizers2,3 and increased nitrogen discharge from livestock, domestic and industrial sources have resulted in pervasive water pollution. Quantifying a nitrogen 'boundary'4 in heterogeneous environments is important for the effective management of local water quality. Here we use a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014. We find that the critical surface-water quality standard (1.0 milligrams of nitrogen per litre) was being exceeded in most provinces by the mid-1980s, and that current rates of anthropogenic nitrogen discharge (14.5 ± 3.1 megatonnes of nitrogen per year) to fresh water are about 2.7 times the estimated 'safe' nitrogen discharge threshold (5.2 ± 0.7 megatonnes of nitrogen per year). Current efforts to reduce pollution through wastewater treatment and by improving cropland nitrogen management can partially remedy this situation. Domestic wastewater treatment has helped to reduce net discharge by 0.7 ± 0.1 megatonnes in 2014, but at high monetary and energy costs. Improved cropland nitrogen management could remove another 2.3 ± 0.3 megatonnes of nitrogen per year-about 25 per cent of the excess discharge to fresh water. Successfully restoring a clean water environment in China will further require transformational changes to boost the national nutrient recycling rate from its current average of 36 per cent to about 87 per cent, which is a level typical of traditional Chinese agriculture. Although ambitious, such a high level of nitrogen recycling is technologically achievable at an estimated capital cost of approximately 100 billion US dollars and operating costs of 18-29 billion US dollars per year, and could provide co-benefits such as recycled wastewater for crop irrigation and improved environmental quality and ecosystem services.

Multi-objective optimization of energy and greenhouse gas emissions in water pumping and treatment.

A large part of operating costs in urban water supply networks is usually due to energy use, mostly in the form of electricity consumption. There is growing pressure to reduce energy use to help save operational costs and reduce carbon emissions. However, in practice, reducing these costs has proved to be challenging because of the complexity of the systems. Indeed, many water utilities have concluded that they cannot practically achieve further energy savings in the operation of their water supply systems. This study shows how a hybrid linear and multi-objective optimization approach can be used to identify key energy consumption elements in a water supply system, and then evaluate the amount of investment needed to achieve significant operational gains at those points in the supply network. In application to the water supply system for the city of London, the method has shown that up to 18% savings in daily energy consumption are achievable. The optimal results are sensitive to discount rate and the financial value placed on greenhouse gas emissions. Valuation of greenhouse gas emissions is necessary to incentivise high levels of energy efficiency. The methodology can be used to inform planning and investment decisions, with specific focus on reducing energy consumption, for existing urban water supply systems.

Quantifying the energy consumption and greenhouse gas emissions of changing wastewater quality standards.

Regulations to ensure adequate wastewater treatment are becoming more stringent as the negative effects of different pollutants on human health and the environment are understood. However, treatment of wastewater to remove pollutants is energy intensive, so has added significantly to the operation costs of wastewater treatment plants. Analysis from six of the largest wastewater treatment works in South East England reveals that the energy consumption of these treatment works has doubled in the last five years due to expansions to meet increasingly stringent effluent standards and population growth. This study quantifies the relationship between energy use for wastewater treatment and four measures of pollution in effluents from UK wastewater treatment works (biochemical oxygen demand, ammoniacal nitrogen, chemical oxygen demand and suspended solids). The linear regression results show that indicators of these pollutants in effluents, together with the extension of plants to improve wastewater treatment, can predict over 95% of energy consumption. Secondly, using scenarios, the energy consumption and greenhouse gas emissions of effluent quality standards are estimated. The study finds that tightening effluent standards to increase water quality could result in a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional MTCO2 per year from treating wastewater in large works in the UK.

A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods.

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber-Physical Attacks on Electricity Distribution Infrastructure Networks.

In December 2015, a cyber-physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber-physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber-physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian-style cyber-physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision-makers wish to mitigate major population disruptions, then they must invest resources more-or-less equally across all substations, to prevent the scaling of a cyber-physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber-physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.

The myriad challenges of the Paris Agreement.

The much awaited and intensely negotiated Paris Agreement was adopted on 12 December 2015 by the Parties to the United Nations Framework Convention on Climate Change. The agreement set out a more ambitious long-term temperature goal than many had anticipated, implying more stringent emissions reductions that have been under-explored by the research community. By its very nature a multidisciplinary challenge, filling the knowledge gap requires not only climate scientists, but the whole Earth system science community, as well as economists, engineers, lawyers, philosophers, politicians, emergency planners and others to step up. To kick start cross-disciplinary discussions, the University of Oxford's Environmental Change Institute focused its 25th anniversary conference upon meeting the challenges of the Paris Agreement for science and society. This theme issue consists of review papers, opinion pieces and original research from some of the presentations within that meeting, covering a wide range of issues underpinning the Paris Agreement.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

A systems framework for national assessment of climate risks to infrastructure.

Extreme weather causes substantial adverse socio-economic impacts by damaging and disrupting the infrastructure services that underpin modern society. Globally, $2.5tn a year is spent on infrastructure which is typically designed to last decades, over which period projected changes in the climate will modify infrastructure performance. A systems approach has been developed to assess risks across all infrastructure sectors to guide national policy making and adaptation investment. The method analyses diverse evidence of climate risks and adaptation actions, to assess the urgency and extent of adaptation required. Application to the UK shows that despite recent adaptation efforts, risks to infrastructure outweigh opportunities. Flooding is the greatest risk to all infrastructure sectors: even if the Paris Agreement to limit global warming to 2°C is achieved, the number of users reliant on electricity infrastructure at risk of flooding would double, while a 4°C rise could triple UK flood damage. Other risks are significant, for example 5% and 20% of river catchments would be unable to meet water demand with 2°C and 4°C global warming respectively. Increased interdependence between infrastructure systems, especially from energy and information and communication technology (ICT), are amplifying risks, but adaptation action is limited by lack of clear responsibilities. A programme to build national capability is urgently required to improve infrastructure risk assessment.This article is part of the theme issue 'Advances in risk assessment for climate change adaptation policy'.

Evaluating the Benefits of Adaptation of Critical Infrastructures to Hydrometeorological Risks.

Infrastructure adaptation measures provide a practical way to reduce the risk from extreme hydrometeorological hazards, such as floods and windstorms. The benefit of adapting infrastructure assets is evaluated as the reduction in risk relative to the "do nothing" case. However, evaluating the full benefits of risk reduction is challenging because of the complexity of the systems, the scarcity of data, and the uncertainty of future climatic changes. We address this challenge by integrating methods from the study of climate adaptation, infrastructure systems, and complex networks. In doing so, we outline an infrastructure risk assessment that incorporates interdependence, user demands, and potential failure-related economic losses. Individual infrastructure assets are intersected with probabilistic hazard maps to calculate expected annual damages. Protection measure costs are integrated to calculate risk reduction and associated discounted benefits, which are used to explore the business case for investment in adaptation. A demonstration of the methodology is provided for flood protection of major electricity substations in England and Wales. We conclude that the ongoing adaptation program for major electricity assets is highly cost beneficial.

Exploring Cooperative Transboundary River Management Strategies for the Eastern Nile Basin.

A water resource modeling process is demonstrated to support multistakeholder negotiations over transboundary management of the Nile River. This process addresses the challenge of identifying management options of new hydraulic infrastructure that potentially affects downstream coriparian nations and how the management of existing infrastructure can be adapted. The method includes an exploration of potential management decisions using a multiobjective evolutionary algorithm, intertwined with an iterative process of formulating cooperative strategies to overcome technical and political barriers faced in a transboundary negotiation. The case study is the addition of the Grand Ethiopian Renaissance Dam (GERD) and considers how its operation may be coordinated with adaptations to the operations of Egypt's High Aswan Dam. The results demonstrate that a lack of coordination is likely to be harmful to downstream riparians and suggest that adaptations to infrastructure in Sudan and Egypt can reduce risks to water supplies and energy generation. Although risks can be substantially reduced by agreed releases from the GERD and basic adaptations to the High Aswan Dam, these measures are still insufficient to assure that no additional risk is assumed by Egypt. The method then demonstrates how improvements to water security for both downstream riparians can be achieved through dynamic adaptation of the operation of the GERD during drought conditions. Finally, the paper demonstrates how the robustness of potential management arrangements can be evaluated considering potential effects of climate change, including increased interannual variability and highly uncertain changes such as increases in the future persistence of droughts.

Geographic Hotspots of Critical National Infrastructure.

Failure of critical national infrastructures can result in major disruptions to society and the economy. Understanding the criticality of individual assets and the geographic areas in which they are located is essential for targeting investments to reduce risks and enhance system resilience. Within this study we provide new insights into the criticality of real-life critical infrastructure networks by integrating high-resolution data on infrastructure location, connectivity, interdependence, and usage. We propose a metric of infrastructure criticality in terms of the number of users who may be directly or indirectly disrupted by the failure of physically interdependent infrastructures. Kernel density estimation is used to integrate spatially discrete criticality values associated with individual infrastructure assets, producing a continuous surface from which statistically significant infrastructure criticality hotspots are identified. We develop a comprehensive and unique national-scale demonstration for England and Wales that utilizes previously unavailable data from the energy, transport, water, waste, and digital communications sectors. The testing of 200,000 failure scenarios identifies that hotspots are typically located around the periphery of urban areas where there are large facilities upon which many users depend or where several critical infrastructures are concentrated in one location.

Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit.

Global climate models suggest an increase in evapotranspiration, changing storm tracks, and moisture delivery in many parts of the world, which are likely to cause more prolonged and severe drought, yet the weakness of climate models in modeling persistence of hydroclimatic variables and the uncertainties associated with regional climate projections mean that impact assessments based on climate model output may underestimate the risk of multiyear droughts. In this paper, we propose a vulnerability-based approach to test water resource system response to drought. We generate a large number of synthetic streamflow series with different drought durations and deficits and use them as input to a water resource system model. Marginal distributions of the streamflow for each month are generated by bootstrapping the historical data, while the joint probability distributions of consecutive months are constructed using a copula-based method. Droughts with longer durations and larger deficits than the observed record are generated by perturbing the copula parameter and by adopting an importance sampling strategy for low flows. In this way, potential climate-induced changes in monthly hydrological persistence are factored into the vulnerability analysis. The method is applied to the London water system (England) to investigate under which drought conditions severe water use restrictions would need to be imposed. Results indicate that the water system is vulnerable to drought conditions outside the range of historical events. The vulnerability assessment results were coupled with climate model information to compare alternative water management options with respect to their vulnerability to increasingly long and severe drought.

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach.

Anthropogenic loading of nitrogen to river systems can pose serious health hazards and create critical environmental threats. Quantification of the magnitude and impact of freshwater nitrogen requires identifying key controls of nitrogen dynamics and analyzing both the past and present patterns of nitrogen flows. To tackle this challenge, we adopted a machine learning (ML) approach and built an ML-driven representation that captures spatiotemporal variability in nitrogen concentrations at global scale. Our model uses random forests to regress a large sample of monthly measured stream nitrogen concentrations onto a set of 17 predictors with a spatial resolution of 0.5-degree over the 1990-2013, including observations within the pixel and upstream drivers. The model was validated with data from rivers outside the training dataset and was used to predict nitrogen concentrations in 520 major river basins of the world, including many with scarce or no observations. We predicted that the regions with highest median nitrogen concentrations in their rivers (in 2013) were: United States (Mississippi), Pakistan, Bangladesh, India (Indus, Ganges), China (Yellow, Yangtze, Yongding, Huai), and most of Europe (Rhine, Danube, Vistula, Thames, Trent, Severn). Other major hotspots were the river basins of the Sebou (Morroco), Nakdong (South Korea), Kitakami (Japan), and Egypt's Nile Delta. Our analysis showed that the rate of increase in nitrogen concentration between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, Pakistan, mainland southeast Asia, and south-eastern Australia. Using a new grouped variable importance measure, we also found that temporality (month of the year and cumulative month count) is the most influential predictor, followed by factors representing hydroclimatic conditions, diffuse nutrient emissions from agriculture, and topographic features. Our model can be further applied to assess strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.

Simulating flexibility, variability and decentralisation with an integrated energy system model for Great Britain.

Energy system models allow the development and assessment of ambitious transition pathways towards a sustainable energy system. However, current models lack adequate spatial and temporal resolution to capture the implications of a shift to decentralised energy supply and storage across multiple local energy vectors to meet spatially variable energy demand. There is also a lack of representation of interactions with the transport sector as well as national and local energy system operation. Here, we bridge these gaps with a high-resolution system-of-systems modelling framework which is applied to Great Britain to simulate differences between the performance of decarbonised energy systems in 2050 through two distinct strategies, an electric strategy and a multi-vector strategy prioritising a mix of fuels, including hydrogen. Within these strategies, we simulated the impacts of decentralised operation of the energy system given the variability of wind and across flexibility options including demand side management, battery storage and vehicle to grid services. Decentralised operation was shown to improve operational flexibility and maximise utilisation of renewables, whose electricity supplies can be cost-effectively converted to hydrogen or stored in batteries to meet peak electricity demands, therefore reducing carbon-intensive generation and the requirement for investment in expanding the electricity transmission network capacity.

Sustainable pathways towards climate and biodiversity goals in the UK: the importance of managing land-use synergies and trade-offs.

Agricultural and environmental policies are being fundamentally reviewed and redesigned in the UK following its exit from the European Union. The UK government and the Devolved Administrations recognise that current land use is not sustainable and that there is now an unprecedented opportunity to define a better land strategy that responds fully to the interconnected challenges of climate change, biodiversity loss and sustainable development. This paper presents evidence from three pathways (current trends, sustainable medium ambition, and sustainable high ambition) to mid-century that were co-created with UK policymakers. The pathways were applied to a national integrated food and land-use model (the FABLE calculator) to explore potential synergies and trade-offs between achieving multiple sustainability targets under limited land availability and constraints to balance food supply and demand at national and global levels. Results show that under the Current Trends pathway all unprotected open natural land would be converted to urban, agriculture and afforested land, with the consequence that from 2030 onwards tree planting targets could not be met. In contrast, the two sustainable pathways illustrate how dietary change, agricultural productivity improvements and waste reduction can free up land for nature recovery and carbon sequestration. This enables a transition to a sustainable food and land-use system that provides a net carbon sink with up to 44% of land able to support biodiversity conservation. We highlight key trade-offs and synergies, which are important to consider for designing and implementing emerging national policies. These include the strong dependence of climate, food and biodiversity targets on dietary shifts, sustainable improvements in agricultural productivity, improved land-use design for protecting and restoring nature, and rapid reductions in food loss and waste. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-022-01242-8.

Imprecise probability assessment of tipping points in the climate system.

Major restructuring of the Atlantic meridional overturning circulation, the Greenland and West Antarctic ice sheets, the Amazon rainforest and ENSO, are a source of concern for climate policy. We have elicited subjective probability intervals for the occurrence of such major changes under global warming from 43 scientists. Although the expert estimates highlight large uncertainty, they allocate significant probability to some of the events listed above. We deduce conservative lower bounds for the probability of triggering at least 1 of those events of 0.16 for medium (2-4 degrees C), and 0.56 for high global mean temperature change (above 4 degrees C) relative to year 2000 levels.

Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods.

This study compares two widely used approaches for robustness analysis of decision problems: the info-gap method originally developed by Ben-Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate-altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info-gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.

Targeting climate adaptation to safeguard and advance the Sustainable Development Goals.

The international community has committed to achieve 169 Sustainable Development Goal (SDG) targets by 2030 and to enhance climate adaptation under the Paris Agreement. Despite the potential for synergies, aligning SDG and climate adaptation efforts is inhibited by an inadequate understanding of the complex relationship between SDG targets and adaptation to impacts of climate change. Here we propose a framework to conceptualise how ecosystems and socio-economic sectors mediate this relationship, which provides a more nuanced understanding of the impacts of climate change on all 169 SDG targets. Global application of the framework reveals that adaptation of wetlands, rivers, cropland, construction, water, electricity, and housing in the most vulnerable countries is required to safeguard achievement of 68% of SDG targets from near-term climate risk by 2030. We discuss how our framework can help align National Adaptation Plans with SDG targets, thus ensuring that adaptation advances, rather than detracts from, sustainable development.

Tipping elements in the Earth's climate system.

The term "tipping point" commonly refers to a critical threshold at which a tiny perturbation can qualitatively alter the state or development of a system. Here we introduce the term "tipping element" to describe large-scale components of the Earth system that may pass a tipping point. We critically evaluate potential policy-relevant tipping elements in the climate system under anthropogenic forcing, drawing on the pertinent literature and a recent international workshop to compile a short list, and we assess where their tipping points lie. An expert elicitation is used to help rank their sensitivity to global warming and the uncertainty about the underlying physical mechanisms. Then we explain how, in principle, early warning systems could be established to detect the proximity of some tipping points.