Mid-term Future Research Articles

Assessing Alterations of Rainfall Variability Under Climate Change in Zengwen Reservoir Watershed, Southern Taiwan

This study aims to detect changes in rainfall variability caused by climate change for various scenarios in the CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble. Projected changes in rainfall unevenness in terms of different timescale indices using three categories, namely WD50 (number of wettest days for half annual rainfall), SI (seasonality index), and DWR (ratio of dry-season to wet-season rainfall) are analyzed in Zengwen Reservoir watershed, southern Taiwan over near future (2021–2040) and midterm future (2041–2060) relative to the baseline period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The projected rainfall for both baseline and future periods is derived from 25 GCMs (global climate models). The results indicate that noticeably deteriorated rainfall unevenness is projected in the Zengwen Reservoir watershed over future periods, which include decreased WD50, increased SI, and decreased DWR. Though there were noticeable differences in the rainfall projections by the different GCMs, the overall consensus reveals that uncertainties in future rainfall should not be ignored. In addition, WD50 has the greatest deviated relative change in mean, which implies that the short-timescale rainfall unevenness index is easily affected by climate change in the study area. Distributional changes in rainfall unevenness determined by simultaneously considering alterations in relative changes in mean and standard deviation indicated that there was no single dominant category. However, the top two categories, with summed frequencies exceeding 0.5, characterize different properties of rainfall unevenness indices. The top two categories of WD50 and SI commonly have decreased mean and increased mean, respectively, but nearly equal frequencies of the top two categories in DWR exhibit opposite variations. The proposed rainfall unevenness change detection approach provides a better understanding of the impacts of climate change on rainfall unevenness, which is useful for preparing adaptive mitigation measures for coping with disasters induced by climate change.

Future changes in agrometeorological extremes in the southern Mediterranean region: When and where will they affect croplands and wheatlands?

Climate change and extremes are increasingly threatening food security, especially in the Global South. Here, we examine how croplands and wheatlands of the southern Mediterranean region could be affected by projected changes in agrometeorological extremes over the 21st century. We use 17 bias-corrected climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to identify potential trends and assess the time of emergence of significant changes in agrometeorological extremes under the Shared Socioeconomic Pathway (SSP3–7.0). We note that simulated historical trends in agrometeorological extremes closely match observed trends, here derived from ERA5land, over croplands. Our analysis of CMIP6 projected scenarios reveals a consistent rise in heat intensity, drought intensity, and the frequency of compound dry and hot (D5/H95) days. While a reduction in frost intensity, combined with fewer wet and cold (W95/C5) and dry and cold (D5/C5) events offer some mitigation potential, concerns about water scarcity due to heightened heat and drought stresses may overshadow these benefits. These changes in agrometeorological extremes are projected to emerge in the near- and mid-term future (by 2030 and 2050). We also note that the projected decreases in cold extremes affect smaller agricultural regions than the increases in extreme heat. We find higher likelihoods of negative agrometeorological impacts over croplands and wheatlands throughout the 21st century, which could significantly challenge crop yields and agricultural sustainability. Without proactive adaptation and mitigation strategies, food security could come increasingly under threat in a changing climate in the southern Mediterranean region.

Subcell‐Resolved Electroluminescence Imaging of Monolithic Perovskite/Silicon Tandem Solar Cell for High‐Throughput Characterization

In the midterm future, the photovoltaic industry is expected to be dominated by two‐terminal (2T) perovskite–silicon (pero–Si) tandem solar cells, which have high energy conversion efficiency and require characterization for large‐scale production. Electroluminescence (EL) imaging is one of the most prevalent and nondestructive techniques for defect detection, recognition, and characterization in Si‐solar cells in mass production. This work presents an EL setup that enables fast, simultaneous, and separate luminescence capture from the two subcells of pero–Si tandem devices. To demonstrate the setup, several encapsulated 2T pero–Si tandem samples are investigated. First, the effect that resistive coupling between the two subcells has on defect appearance in EL images is recorded. Therefore, EL image under different operational conditions is recorded. A strong dependence of defect signatures on current injection is observed, that is explained partly by resistive coupling but partly as well by injection‐dependent changes of the prevalent defects in the cells. An investigation of preconditioning under dark forward operation reveals significant local decrease of EL intensity going along with rapid reversible or irreversible and severe degradation close to the edges of the samples. This degradation takes place under forward bias during a period of ≈1 h.

Comparative assessment of dry- and humid-heat extremes in a warming climate: Frequency, intensity, and seasonal timing

Dry and humid-heat extremes are two types of heat extremes, each exhibiting unique climatological characteristics and impacts on different sectors of society. Using historical simulations and projections produced under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) by models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show a comparative assessment of the future changes in dry- and humid-heat extremes over global land. Relative to 1995–2014, projections for the mid-term future (2041–2060) and long-term future (2081–2100) periods suggest that most global regions will experience an increase in frequency and intensity of both dry- and humid-heat extremes, especially the tropical regions. In these future periods, the peak occurrences of dry- and humid-heat extremes in mid-to high-latitude regions often occur within the same month. However, there will be a one-to two-month gap between the peak occurrences of dry- and humid-heat extremes in tropical regions, primarily due to monsoonal circulations that introduce variability by causing dry-heat extremes before the onset of monsoons and humid-heat extremes as the monsoons commence. This suggests the need for sector-specific adaptation strategies during different periods of the year for tropical regions. Under both future scenarios, whether considering individual exposure or land area, the average level of exposure to extreme humid-heat days is projected to increase more significantly compared to dry-heat days. The above results highlight the risks associated with the intensification of humid heat in future climate scenarios and warrant the development of effective strategies to mitigate the adverse effects.

Climate projections of the Adriatic Sea: role of river release

The Adriatic Sea, characterized by unique local features in comparison to the broader Mediterranean Sea, stands out as a highly susceptible region to climate change. In this context, our study involves a focused climate downscaling approach, concentrating on the Adriatic water cycle. This encompasses integrated modeling at the mesoscale, covering the atmosphere, hydrology, and marine general circulation. The study period spans from 1992 to 2050, considering the high emission scenario RCP8.5. We aim at evaluating how the river release projection affects the local density stratification and the sea level rise. Indeed, the river release is found to decrease by approximately 35% in the mid-term future and condition the stratification of the water column with differences between the Northern and Southern sub-basins. The projected runoff decrease has a major impact on the Northern sub-basin, where the stratification is haline-dominated and the foreseen salinization prevails on the heating through the whole water column. Conversely, the runoff decrease has a lower impact on the Southern sub-basin, where the future changes of other mechanisms may play a major role, e.g., the changing properties of the Mediterranean water entering the Otranto Strait and the foreseen heating prevails on the salinization from the intermediate to deep water column. The study provides the first evidence of how the decreasing river discharge locally reduces the density stratification, increases the dense water, and mitigates the sea level rise in the Northern Adriatic Sea, thus acting in the opposite direction to the global warming. To minimize uncertainty in coastal ocean projections around the world, it is essential that the climate downscaling integrates high-resolution hydrology and hydrodynamics models to correctly reproduce the link between surface buoyancy and stratification and the resulting dynamics.

Defining weather scenarios for simulation-based assessment of thermal resilience of buildings under current and future climates: A case study in Brazil

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

Lattice Boltzmann–Carleman quantum algorithm and circuit for fluid flows at moderate Reynolds number

We present a quantum computing algorithm for fluid flows based on the Carleman-linearization of the Lattice Boltzmann (LB) method. First, we demonstrate the convergence of the classical Carleman procedure at moderate Reynolds numbers, namely, for Kolmogorov-like flows. Then we proceed to formulate the corresponding quantum algorithm, including the quantum circuit layout, and analyze its computational viability. We show that, at least for moderate Reynolds numbers between 10 and 100, the Carleman–LB procedure can be successfully truncated at second order, which is a very encouraging result. We also show that the quantum circuit implementing the single time-step collision operator has a fixed depth, regardless of the number of lattice sites. However, such depth is of the order of ten thousands quantum gates, meaning that quantum advantage over classical computing is not attainable today, but could be achieved in the near or mid-term future. The same goal for the multi-step version remains, however, an open topic for future research.

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

UK and Global Food Security in the Era Of ‘Permacrisis’

The UK’s food security is currently dependent on complex international supply chains that are under threat from climate change, conflicts and geopolitical stressors; at the same time, climate change and the repercussions of Brexit challenge the UK’s ability to increase self-reliance and internal resilience. Jennifer Cole and Ivica Petrikova examine the key drivers and pressures on global food security and how this is likely to be impacted in the near- and mid-term future, including by shifting geopolitical alliances.■

Benefits assessment of cool skin and ventilated cavity skin: Saving energy and mitigating heat and grid stress

This study assessed the energy-saving and climate-adaptive potential of cool skin and ventilated cavity skin facade technologies in Seoul's high-rise apartment buildings. We created weather scenarios for historical, mid-term future, and long-term future conditions using Coordinated Regional Downscaling EXperiment (CORDEX) method. Building energy simulations were conducted on a South Korean high-rise apartment model to evaluate their performance under different weather conditions. The results indicate that cool skin and ventilated cavity skin technologies can save cooling energy during summers but lead to heating energy penalties in winters. Ventilated cavity skin outperforms cool skin, offering better cooling energy savings and reduced heating penalties. Combining both technologies yields the highest overall energy savings, with 7 %, 9 %, and 10 % cooling energy savings for cool skin, ventilated cavity skin, and the combined package, respectively. However, cool skin increases heating energy consumption by 5 %, while ventilated cavity skin has minimal impact on heating energy. These envelope technologies also reduce peak electricity demand by at least 5 %, 8 %, and 9 %, respectively. They contribute to heat stress reduction, enhance resilience, and decrease extreme heat risks for occupants during power outages by at least 18 % under various weather conditions. Considering the prevalence of aging high-rise apartments in South Korea, adopting these envelope renovation strategies can effectively reduce cooling loads, enhance thermal comfort, and boost resilience under future climates, while avoiding costly reconstruction.

Quantum-Inspired Digital Annealing for Join Ordering

Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore's law. We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.

Comparison of Space Cooling Systems from Energy and Economic Perspectives for a Future City District in Sweden

In this study, the performance of different cooling technologies from energy and economic perspectives were evaluated for six different prototype residential Nearly Zero Energy Buildings (NZEBs) within a planned future city district in central Sweden. This was carried out by assessing the primary energy number and life cycle cost analysis (LCCA) for each building model and cooling technology. Projected future climate file representing the 2050s (mid-term future) was employed. Three cooling technologies (district cooling, compression chillers coupled/uncoupled with photovoltaic (PV) systems, and absorption chillers) were evaluated. Based on the results obtained from primary energy number and LCCA, compression chillers with PV systems appeared to be favorable as this technology depicted the least value for primary energy use and LCCA. Compared to compression chillers alone, the primary energy number and the life cycle cost were reduced by 13%, on average. Moreover, the district cooling system was found to be an agreeable choice for buildings with large floor areas from an economic perspective. Apart from these, absorption chillers, utilizing environmentally sustainable district heating, displayed the highest primary energy use and life cycle cost which made them the least favorable choice. However, the reoccurring operational cost from the LCCA was about 60 and 50% of the total life cycle cost for district cooling and absorption chillers, respectively, while this value corresponds to 80% for the compression chillers, showing the high net present value for this technology but sensitive to future electricity prices.

What’s Next? Historical Research on the GDR Three Decades after German Unification

Abstract The once-heated controversies about the history of the East German state and its legacy in the post-unification years have finally come to an end, some historians of the GDR have recently emphasized. Picking up on this cue, this forum enquires how a potential new openness might shape historians’ research agendas in the mid-term future. To make such predictions, we start by reflecting on our own intellectual formation and how we became interested in the histories of the GDR. We then discuss some of the latest developments in the field before sketching out pathways for future research. These observations and predictions are necessarily subjective. We do not aim at a comprehensive overview but want to provide a snapshot of the diversity in the field and its various generational expressions, highlighting new trends.

Patent landscape analysis-Contributing to the identification of technology trends and informing research and innovation funding policy.

Patents and the systematic analysis thereof provide important decision information for a range of stakeholders pursuing diverse goals. However, in particular academia and small-scale private sector innovators underappreciate the value of patent information for identifying research gaps, ensuring originality of their work and, in turn, maximisation of limited (public) funds. By the same token, pertinent public sector organisations, such as regulators, require overviews of potentially upcoming technologies to adequately adapt regulatory protocols. The latter, in particular if contemporary scientific evidence is not sufficient, can require a substantial amount of time and lead to a delay in the marketing of important innovations. Patent landscape analysis (PLA) is a very useful method to create overviews of technology fields and thereby indicate if, inter alia, a specific line of scientific enquiry and its application have already been pursued or potential regulatory gaps will exist in the near- to mid-term future. The objective of this communication is to increase awareness of the utility of patent information and provide support in retrieval and analysis of pertinent information for those involved in biotech R&D. Based on Espacenet, a patent search engine, we provide basic guidance on search strategy development, piloting and execution, and data preparation and analysis. To highlight the value of PLA, we also summarise selected results of a PLA we performed recently for microbiome targeting interventions, also referred to as live biotherapeutic products.

Near- and Mid-term Future of the LHC Experiments

Near- and Mid-term Future of the LHC Experiments

Status Of Air Quality In Tirana During Quarantine Imposed Due To Covid-19 Global Pandemic

This research paper builds on the analytical work conducted by Co-PLAN, Institute for Habitat Development under the “Green Lungs for Our Cities” project, supported by the EU Delegation in Albania. This specific publication consists on the analysis of data from a dedicated ground-based monitoring process conducted from March 11th to May 11th, 2020 in 4 different locations of Tirana on PM2.5, PM10 and NO2, based on the protocol and practice established by the Alternative Monitoring Methodology of the Green Lungs project (link). Other sources, such as publications from World Bank “Regional Note on Air Quality Management in Western Balkans”, +IQAir “2019 World Air Quality Report”, European Environmental Agency “Air Quality in Europe – 2019 report” and “Assessing air quality through citizens science”, were also consulted during the analysis of data from this 62-days monitoring practice. The data provide an overview of how concentration levels of the selected air pollutants changed during the lockdown phase and after the removal of measures. The purpose of the monitoring process and of the report was threefold: (i)to verify the direct linkages between air pollutants and sources of pollution, (ii)raise awareness on air pollution among state and non-state actors, (iii)propose relevant measures to reduce urban air pollution for the near to mid-term future.

Renewable methanol production from green hydrogen and captured CO2: A techno-economic assessment

This paper aims to present a pre-feasibility study of a power-to-fuel plant configuration designed for the production of 500 kg/h of renewable methanol (e-methanol) from green hydrogen and captured carbon dioxide. Hydrogen is obtained by water electrolysis employing the overproduction of renewable electricity. Carbon dioxide is assumed to be separated from the flue gas of a conventional power station by means of an amine-based CO2 absorption system. A comprehensive process model has been developed with the support of Aspen Plus tool to simulate all the plant sections and the overall system. After the process optimization, a detailed economic analysis – based on capital and operating costs derived from commercial-scale experience and assuming a 20-year lifetime – has been performed to calculate a levelized cost of methanol (LCoM) of 960 €/t (about 175 €/MWh). The analysis confirms that, today, the technology is still not competitive from the economic point of view, being LCoM more than double than the current methanol price in the international market (450 €/t). However, it indicates that the process is expected to become competitive in a mid-term future, as a consequence of the new European policies. The study also reveals that LCoM is mainly affected by the electricity price and the electrolyser capital cost, as well as the capacity factor of the plant.

The effects of structural and technical change on China’s industrial CO2 emissions pathways under uncertainty

ABSTRACT The industrialization process in China has resulted in the fast growth of the country’s energy consumption and CO2 emissions. Examining the effects of industrial structural change on the emissions pathways in the mid-term future would help advance understanding of how industrial policy choices affect the fulfillment of the strategic climate targets of emissions peaking and carbon neutrality. This study couples index decomposition analysis (IDA) with an additive nonparametric regression model to project the possible emissions pathways with different industrial structures. It develops a set of scenarios following the storylines of shared socioeconomic pathways (SSPs) to examine these effects in an uncertain environment towards 2040. The results show that structural change has played an increasing role in curbing carbon emissions of China’s industrial sectors since 2000. The CO2 emissions reductions attributable to this effect were 686 million tons (Mton CO2) between 2000 and 2013, and these contributions to emission mitigation rose to 798 Mton between 2014 and 2019. The scenario results suggest that the aggregated effect of energy efficiency and structure upgrades will decrease emissions by 43% in 2040 relative to the level in 2019 in the ideal case. Regardless of the uncertainties in scenario settings, heavy industry will continue to dominate China’s industrial emissions through 2040. Nevertheless, a significant structural change with an increased share of high-tech industries, such as information and communication technology, could lead to more than a 30% reduction in emissions compared to cases with more minor changes in this sub-sector. Key Policy Insights A rapid expansion of heavy industry is the primary factor driving the rapid growth of China’s industrial CO2 emissions since 2000. Industrial structural change is the second most significant factor curbing emissions growth, and the influence of this factor is increasing. Keeping up the momentum of structural change and technological upgrades in the industrial sector would make it possible for industry emissions to decrease in the medium-term future, therefore contributing substantially to China’s goal of peaking its CO2 emissions by 2030. Realizing carbon neutrality in the longer term necessitates not only structural change in industry, but also a fundamental transformation of the energy supply system.

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms.

Quantum Machine Learning (QML) has not yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum computing are promising, considering a mid-term future (not taking into account the achievements purely associated with optimization using quantum-classical algorithms). The current quantum computers are noisy and have few qubits to test, making it difficult to demonstrate the current and potential quantum advantage of QML methods. This study shows that we can achieve better classical encoding and performance of quantum classifiers by using Linear Discriminant Analysis (LDA) during the data preprocessing step. As a result, the Variational Quantum Algorithm (VQA) shows a gain of performance in balanced accuracy with the LDA technique and outperforms baseline classical classifiers.

Main group catalysis for H2 purification based on liquid organic hydrogen carriers.

Molecular hydrogen (H2) is one of the most important energy carriers. In the midterm future, a huge amount of H2 will be produced from a variety of hydrocarbon sources through conversion and removal of contaminants such as CO and CO2. However, bypassing these purification processes is desirable, given their energy consumption and environmental impact, which ultimately increases the cost of H2. Here, we demonstrate a strategy to separate H2 from a gaseous mixture of H2/CO/CO2/CH4 that can include an excess of CO and CO2 relative to H2 and simultaneously store it in N-heterocyclic compounds that act as liquid organic hydrogen carriers (LOHCs), which can be applied to produce H2 by subsequent dehydrogenation. Our results demonstrate that LOHCs can potentially be used for H2 purification from CO- and CO2-rich crude H2 in addition to their well-established use in H2 storage.