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Abstract Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.

Abstract In Alzheimer’s disease, amyloid-beta (Aβ) triggers the trans-synaptic spread of tau pathology, and aberrant synaptic activity has been shown to promote tau spreading. Aβ induces aberrant synaptic activity, manifesting in increases in the presynaptic growth-associated protein 43 (GAP-43), which is closely involved in synaptic activity and plasticity. We therefore tested whether Aβ-related GAP-43 increases, as a marker of synaptic changes, drive tau spreading in 93 patients across the aging and Alzheimer’s spectrum with available CSF GAP-43, amyloid-PET and longitudinal tau-PET assessments. We found that (1) higher GAP-43 was associated with faster Aβ-related tau accumulation, specifically in brain regions connected closest to subject-specific tau epicenters and (2) that higher GAP-43 strengthened the association between Aβ and connectivity-associated tau spread. This suggests that GAP-43-related synaptic changes are linked to faster Aβ-related tau spread across connected regions and that synapses could be key targets for preventing tau spreading in Alzheimer’s disease.

Abstract The growing demand for digital investment advisory services and the advancing technological process led to increased attention to this topic in recent literature. In light of these developments, the question arises whether conventional and digital advisors behave differently in their investment advisory decisions. I therefore conducted a systematic literature review and evaluated 97 publications on the determinants of conventional and digital investment advisory decisions. Based on the literature, five main determinants were identified that are important for investment advisory decisions. These determinants are identical for both variants of the advice, but there are differences in the way they are addressed. This systematic literature review provides an overview of the current state of research and can therefore help identify areas where investment advice can be improved in the future.

Abstract Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein’s unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist.

Abstract Different approaches to quantum gravity converge in predicting the existence of a minimal scale of length. This raises the fundamental question as to whether and how an intrinsic limit to spatial resolution can affect quantum mechanical observables associated to internal degrees of freedom. We answer this question in general terms by showing that the spin operator acquires a momentum-dependent contribution in quantum mechanics equipped with a minimal length. Among other consequences, this modification induces a form of quantum nonlocality stronger than the one arising in ordinary quantum mechanics. In particular, we show that violations of the Bell inequality can exceed the maximum value allowed in ordinary quantum mechanics, the so-called Tsirelson bound, by a positive-valued function of the momentum operator. We introduce possible experimental settings based on neutron interferometry and quantum contextuality, and we provide preliminary estimates on the values of the physical parameters needed for actual laboratory implementations.

In this paper, we investigate the consequences of maximal length as well as minimal momentum scales on nonlocal correlations shared by two parties of a bipartite quantum system. To this aim, we rely on a general phenomenological scheme which is usually associated with the non-negligible spacetime curvature at cosmological scales, namely the extended uncertainty principle. In so doing, we find that quantum correlations are degraded if the deformed quantum mechanical model mimics a positive cosmological constant. This opens up the possibility to recover classicality at sufficiently large distances.

Background: Morbidity burdens from ambient air pollution are associated with market and non-market costs and are therefore important for policymaking. The estimation of morbidity burdens is based on concentration–response functions (CRFs). Most existing CRFs for short-term exposures to PM2.5 assume a fixed risk estimate as a log-linear function over an extrapolated exposure range, based on evidence primarily from Europe and North America. Objectives: We revisit these CRFs by performing a systematic review for seven morbidity endpoints previously assessed by the World Health Organization, including data from all available regions. These endpoints include all cardiovascular hospital admission, all respiratory hospital admission, asthma hospital admission and emergency room visit, along with the outcomes that stem from morbidity, such as lost work days, respiratory restricted activity days, and child bronchitis symptom days. Methods: We estimate CRFs for each endpoint, using both a log-linear model and a nonlinear model that includes additional parameters to better fit evidence from high-exposure regions. We quantify uncertainties associated with these CRFs through randomization and Monte Carlo simulations. Results: The CRFs in this study show reduced model uncertainty compared with previous CRFs in all endpoints. The nonlinear CRFs produce more than doubled global estimates on average, depending on the endpoint. Overall, we assess that our CRFs can be used to provide policy analysis of air pollution impacts at the global scale. It is however important to note that improvement of CRFs requires observations over a wide range of conditions, and current available literature is still limited. Discussion: The higher estimates produced by the nonlinear CRFs indicates the possibility of a large underestimation in current assessments of the morbidity impacts attributable to air pollution. Further studies should be pursued to better constrain the CRFs studied here, and to better characterize the causal relationship between exposures to PM2.5 and morbidity outcomes.

Abstract Minimal and maximal uncertainties of position measurements are widely considered possible hallmarks of low-energy quantum as well as classical gravity. While General Relativity describes interactions in terms of spatial curvature, its quantum analogue may also extend to the realm of curved momentum space as suggested, e.g. in the context of Relative Locality in Deformed Special Relativity. Drawing on earlier work, we show in an entirely Born reciprocal, i.e. position and momentum space covariant, way that the quadratic Generalized Extended Uncertainty principle can alternatively be described in terms of quantum dynamics on a general curved cotangent manifold. In the case of the Extended Uncertainty Principle the curvature tensor in position space is proportional to the noncommutativity of the momenta, while an analogous relation applies to the curvature tensor in momentum space and the noncommutativity of the coordinates for the Generalized Uncertainty Principle. In the process of deriving this map, the covariance of the approach constrains the admissible models to an interesting subclass of noncommutative geometries which has not been studied before. Furthermore, we reverse the approach to derive general anisotropically deformed uncertainty relations from general background geometries. As an example, this formalism is applied to (anti)-de Sitter spacetime.

Abstract Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.

Abstract Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning (DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline’s ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding RED-CNN/QAE, two well-established DL-based denoisers in our pipeline, the denoising performance is improved by 10%/82% (RMSE) and 3%/81% (PSNR) in regions containing metal and by 6%/78% (RMSE) and 2%/4% (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines.

In this paper, we clarify a foundational loose end affecting the phenomenological approach to quantum gravity centered around the generalization of Heisenberg's uncertainty principle. This misconception stems from a series of recently published works in which perturbative and non-perturbative methods are confused, thereby resulting in a blurring of the distinction between changes in the deformed algebra and changes in the representation of operators. Accordingly, this reasoning would render the existence of a minimal length representation-dependent, and thus unphysical.

Rhode Island is the smallest and the second-most densely populated state in the USA. It is also one of the most severely affected states on the Eastern Seaboard due to Climate Change. While Rhode Island residents pay some of the highest energy prices in the country, its less resourceful suburban residents around the capital city, Providence, are exposed to harmful heat island effects during the summer months. As water and wastewater utility operators are often the largest energy consumers in American municipalities, water, and energy consumption exhibit directly evident connections in urban environments. In this study, we employ an Urban-Water-Energy (UWE)-Nexus approach to understand water and energy demand and their interactions in both space and time for Rhode Island; we quantify the extent and exposure of the Urban Heat Island effects among its towns and urban populations using high-resolution details.

Abstract We present a formalism which allows for the perturbative derivation of the Extended Uncertainty Principle (EUP) for arbitrary spatial curvature models and observers. Entering the realm of small position uncertainties, we derive a general asymptotic EUP. The leading 2nd order curvature induced correction is proportional to the Ricci scalar, while the 4th order correction features the 0th order Cartan invariant $$\Psi _2$$ Ψ2 (a scalar quadratic in curvature tensors) and the curved space Laplacian of the Ricci scalar all of which are evaluated at the expectation value of the position operator i.e. the expected position when performing a measurement. This result is first verified for previously derived homogeneous space models and then applied to other non-trivial curvature related effects such as inhomogeneities, rotation and an anisotropic stress fluid leading to black hole “hair”. Our main achievement combines the method we introduce with the Generalized Uncertainty Principle (GUP) by virtue of deformed commutators to formulate a generic form of what we call the Asymptotic Generalized Extended Uncertainty Principle (AGEUP).

Abstract We find exact formulas for the Extended Uncertainty Principle (EUP) for the Rindler and Friedmann horizons and show that they can be expanded to obtain asymptotic forms known from the previous literature. We calculate the corrections to Hawking temperature and Bekenstein entropy of a black hole in the universe due to Rindler and Friedmann horizons. The effect of the EUP is similar to the canonical corrections of thermal fluctuations and so it rises the entropy signalling further loss of information.

Aerosol impacts have not been comprehensively considered in the cost-benefit integrated assessment models that are widely used to analyze climate policy. Here the authors account for these impacts and find that the health co-benefits from improved air quality outweigh the co-harms from increased near-term warming, and that optimal climate policy results in immediate net benefits globally.

Recent publications have raised concerns regarding the actual feasibility Negative Emission Technologies (NETs). Here the authors commented on the financial viability of large-scale late century NETs and suggested that expenditure peak will occur in the end of the century, which would require massive global subsidy program.

The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer’s disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.

Reducing greenhouse gas emissions has the ‘co-benefit’ of also reducing air pollution and associated impacts on human health. Here, we incorporate health co-benefits into estimates of the optimal climate policy for three different climate policy regimes. The first fully internalizes the climate externality at the global level via a uniform carbon price (the ‘cooperative equilibrium’), thus minimizing total mitigation costs. The second connects to the concept of ‘common but differentiated responsibilities’ where nations coordinate their actions while accounting for different national capabilities considering socioeconomic conditions. The third assumes nations act only in their own self-interest. We find that air quality co-benefits motivate substantially reduced emissions under all three policy regimes, but that some form of global cooperation is required to prevent runaway temperature rise. However, co-benefits do warrant high levels of mitigation in certain regions even in the self-interested case, suggesting that air quality impacts may expand the range of possible policy outcomes whereby global temperatures do not increase unabated.

Solar photovoltaic (PV) electricity generation can greatly reduce both air pollutant and greenhouse gas emissions compared to fossil fuel electricity generation. The Chinese government plans to greatly scale up solar PV installation between now and 2030. However, different PV development pathways will influence the range of air quality and climate benefits. Benefits depend on how much electricity generated from PV is integrated into power grids and the type of power plant displaced. Using a coal-intensive power sector projection as the base case, we estimate the climate, air quality, and related human health benefits of various 2030 PV deployment scenarios. We use the 2030 government goal of 400 GW installed capacity but vary the location of PV installation and the extent of inter-provincial PV electricity transmission. We find that deploying distributed PV in the east with inter-provincial transmission maximizes potential CO _2 reductions and air quality-related health benefits (4.2% and 1.2% decrease in national total CO _2 emissions and air pollution-related premature deaths compared to the base case, respectively). Deployment in the east with inter-provincial transmission results in the largest benefits because it maximizes displacement of the dirtiest coal-fired power plants and minimizes PV curtailment, which is more likely to occur without inter-provincial transmission. We further find that the maximum co-benefits achieved with deploying PV in the east and enabling inter-provincial transmission are robust under various maximum PV penetration levels in both provincial and regional grids. We find large potential benefits of policies that encourage distributed PV deployment and facilitate inter-provincial PV electricity transmission in China.

Water is needed for hydroelectric generation and to cool thermoelectric power plants. This dependence on water makes electricity generation vulnerable to droughts. Furthermore, because power sector CO _2 emissions amount to approximately one third of total US emissions, droughts could influence the inter-annual variability of state- and national-scale emissions. However, the magnitude of drought-induced changes in power sector emissions is not well understood, especially in the context of climate mitigation policies. Using multivariate linear regressions, we find that droughts are positively correlated to increases in electricity generation from natural gas in California, Idaho, Oregon, and Washington; and from coal in Colorado, Montana, Oregon, Utah, Washington, and Wyoming. Using a statistical model, we estimate that this shift in generation sources led to total increases in regional emissions of 100 Mt of CO _2 , 45 kt of SO _2 , and 57 kt of NO _x from 2001 to 2015, most of which originated in California, Oregon, Washington, and Wyoming. The CO _2 emissions induced by droughts in California, Idaho, Oregon, and Washington amounted to 7%–12% of the total CO _2 emissions from their respective power sectors, and the yearly rates were 8%–15% of their respective 2030 yearly targets outlined in the Clean Power Plan (CPP). Although there is uncertainty surrounding the CPP, its targets provide appropriate reference points for climate mitigation goals for the power sector. Given the global importance of hydroelectric and thermoelectric power, our results represent a critical step in quantifying the impact of drought on pollutant emissions from the power sector—and thus on mitigation targets—in other regions of the world.

This paper analyses the emissions and cost impacts of mitigation of non-CO2 greenhouse gases (GHGs) at a global level, in scenarios aimed at meeting a range of long-term temperature goals (LTTGs). The study combines an integrated assessment model (TIAM-Grantham) representing CO2 emissions (and their mitigation) from the fossil fuel combustion and industrial sectors, coupled with a model covering non-CO2 emissions (GAINS), using the latest global warming potentials from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report. We illustrate that in general non-CO2 mitigation measures are less costly than CO2 mitigation measures, with the majority of their abatement potential achievable at US2005$100/tCO2e or less throughout the 21st century (compared to a marginal CO2 mitigation cost which is already greater than this by 2030 in the most stringent mitigation scenario). As a result, the total cumulative discounted cost over the period 2010–2100 (at a 5% discount rate) of limiting global average temperature change to 2.5 °C by 2100 is $48 trillion (about 1.6% of cumulative discounted GDP over the period 2010–2100) if only CO2 from the fossil fuel and industrial sectors is targeted, whereas the cost falls to $17 trillion (0.6% of GDP) by including non-CO2 GHG mitigation in the portfolio of options—a cost reduction of about 65%. The criticality of non-CO2 mitigation recommends further research, given its relatively less well-explored nature when compared to CO2 mitigation.

Industrial processes currently contribute 40% to global CO2 emissions and therefore substantial increases in industrial energy efficiency are required for reaching the 2 °C target. We assess the macroeconomic effects of deploying low carbon technologies in six energy intensive industrial sectors (Petroleum, Iron and Steel, Non-metallic Minerals, Paper and Pulp, Chemicals, and Electricity) in Europe, China and India in 2030. By combining the GAINS technology model with a macroeconomic computable general equilibrium model, we find that output in energy intensive industries declines in Europe by 6% in total, while output increases in China by 11% and in India by 13%. The opposite output effects emerge because low carbon technologies lead to cost savings in China and India but not in Europe. Consequently, the competitiveness of energy intensive industries is improved in China and India relative to Europe, leading to higher exports to Europe. In all regions, the decarbonization of electricity plays the dominant role for mitigation. We find a rebound effect in China and India, in the size of 42% and 34% CO2 reduction, respectively, but not in Europe. Our results indicate that the range of considered low-carbon technology options is not competitive in the European industrial sectors. To foster breakthrough low carbon technologies and maintain industrial competitiveness, targeted technology policy is therefore needed to supplement carbon pricing.

Greenhouse gas and air pollutant precursor emissions have been increasing rapidly in India. Large uncertainties exist in emissions inventories and quantification of their uncertainties is essential for better understanding of the linkages among emissions and air quality, climate, and health. We use Monte Carlo methods to assess the uncertainties of the existing carbon dioxide (CO _2 ), carbon monoxide (CO), sulfur dioxide (SO _2 ), nitrogen oxides (NO _x ), and particulate matter (PM) emission estimates from four source sectors for India. We also assess differences in the existing emissions estimates within the nine subnational regions. We find large uncertainties, higher than the current estimates for all species other than CO, when all the existing emissions estimates are combined. We further assess the impact of these differences in emissions on air quality using a chemical transport model. More efforts are needed to constrain emissions, especially in the Indo-Gangetic Plain, where not only the emissions differences are high but also the simulated concentrations using different inventories. Our study highlights the importance of constraining SO _2 , NO _x , and NH _3 emissions for secondary PM concentrations.

The recent paper by Girod et al (2013) analyses the implications of stringent global GHG mitigation targets for the intensities of, inter alia , broad consumption categories like food, shelter and transport. This type of scenario modeling analysis and inverse reasoning helps us to better understand the potential or required contribution of changes in consumption patterns to mitigation. This is welcome because while there is a growing literature on the behavioral and consumption dimensions of mitigation, there is still no widely accepted framework for studying systematically the interactions between supply and demand, behavior and technology, production and consumption. So we are left with the question: what do we need to do exactly to stabilize GHG concentrations? Intuitively, we take our cue from Aristotelian logic: if A implies B, then in order to avoid B we had better prevent A. At this level it is clear that we need either to decarbonize our energy systems to start with, or to suck out CO _2 from the atmosphere. When multiple causes are at work, however, our neat Aristotelian picture is no longer appropriate (Cartwright 2003). Leaving capturing and storage aside, we need to decarbonize our systems, but we also need to reduce the energy intensity, change our personal habits, eat less meat, use more public transportation, etc. What is the right balance between these factors? Can we do just one thing, say, eat less meat, but not another, and still achieve some pretty ambitious mitigation goals? In other words, what are necessary and what are sufficient sets of measures to reach these goals? Let us first look at the question of necessary measures. This gets tricky when applied to individual consumers: it is somewhat akin to the notorious question whether a heap of sand is still a heap when you take away one grain (Sainsbury 2011). If you are inclined to say yes, think once more. What happens when you take away another one, and another one, and another one, and so forth? Eventually you are forced call a single grain a heap. By a similar type of reasoning none of us consumers makes any difference individually. It is tempting to conclude that therefore consumption side mitigation is not sufficient. But it also does not really seem necessary in the strict sense of the word as long as some supply side measure can compensate for a demand side measure not taken. Thus each one of us could go on as before, as long as someone else or some technology is compensating for our own failure to change. To be sure, such elusive argument is, to say the least, not very helpful, but it highlights the difficulty to derive very specific courses of action from aggregate goals. So it takes a more prescriptive approach to get things going. The pragmatic mitigation wedge analysis by, e.g., Pacala and Socolow (2004) has highlighted that a relatively small number of dedicated and practicable measures is sufficient to achieve deep emission cuts, but the balance of these measures in the analyses is understandably somewhat arbitrary. Other analysis, based on Integrated Assessment Models (IAMs) has focused more specifically on the questions of where and when measures would be implemented in the most cost-effective manner. From such studies one can learn about carbon price trajectories, technology diffusion rates, and possibly about conditional probabilities for reaching targets over time. However, IAMs are rarely used to assess systematically the necessary or sufficient conditions for reaching a given target, and when they do the outcome often is—with the occasional exception—disappointingly generic. Moreover, the controversies arising from value-laden allocations derived from IAMs are well-known: in these models emissions are typically reduced where it (supposedly) can be done cheapest, i.e. in low-wage countries, or according to some burden sharing scheme. The allocation of mitigation over time is essentially determined by the magnitude of the discount rate and thus a valuation of future versus present expenditures. Refreshingly, Girod et al (2013) discuss a selection of allocation schemes across sectors, including consumers, that allow us to get an impression of the requirements and bounds for each of a set of stylized demand activities within the context of a plausible overall IAM story. Thus Girod et al (2013), make progress in addressing consumer behavior in the context of a wider set of activities contributing to GHG emissions and technological options to reduce these, without being committed to any particular allocation scheme. Further work will have to address issues raised by a recent study (Schweizer and Kriegler 2012) on the limitations of the scenario space in earlier IPCC assessments to avoid past omissions. Moreover, IAMs in general need to become more transparent and more responsive to the needs of stakeholders. They also need to be applied specifically to identify concrete incentives, such as co-benefits of mitigation (Wagner 2012) and mechanisms (beyond stylized carbon markets) that nudge us towards low emission pathways. References Cartwright N 2003 Hunting Causes and Using Them: Approaches in Philosophy and Economics 1st edn (Cambridge: Cambridge University Press) Girod B, Van Vuuren D P and Hertwich E G 2013 Global climate targets and future consumption level: an evaluation of the required GHG intensity Environ. Res. Lett. 8 014016 Pacala S and Socolow R 2004 Stabilization wedges: solving the climate problem for the next 50 years with current technologies Science 305 968–72 Sainsbury R M 2011 Paradoxes 3rd edn (Cambridge: Cambridge University Press) Schweizer V J and Kriegler E 2012 Improving environmental change research with systematic techniques for qualitative scenarios Environ. Res. Lett. 7 044011 Wagner F 2012 Mitigation here and now or there and then: the role of co-benefits Carbon Manag. 3 325–7