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The digital economy is an alternative to the traditional economy, an area of the future on which investment and RD efforts are focused both by European forums and by Member States, which have understood the importance of the domain with the onset of the pandemic crisis. The aim of the research is to analyze and predict, on the one hand, the impact of digitalisation on EU Member States’ economies by means of the three scenarios for the evolution of the digital component of the economy for the horizon 2025 (the baseline scenario, the high growth scenario and the challenge scenario), and, on the other hand, the Member States’ ability to achieve the targets proposed by these scenarios. The analysis covers the period 2013-2025 and quantifies the dynamics of the digitalisation phenomena and processes based on dedicated statistical analyses (frequency series analysis, application of the unicriterion critical probability test, application of the Enter method, performing Pearson correlation tests) by means of the IBM-SPSS 25 software. The purpose of this research is the provision of relevant solutions to decision makers in the development of digitalisation. The study highlighted the placing of the results in favourable scenarios, the current trend regarding digital economy evolution, and presented the most likely scenario to be achieved in terms of knowing the provider offer and the needs of service users. The topicality of the study targets a new approach on the foundations of financial allocations for the sustainable development of the digital economy needed in the current conditions of the global crisis and of the pandemic for the implementation of digital economy development policies. A novelty of this research is the conceptualization, validation and testing of an econometric model capable of quantifying the realism of the scenarios proposed by the European Union regarding the development of the digital economy.

Seven years of civil war have left Syria devastated, resulting in over half-a-million casualties and precipitating the largest humanitarian crisis in modern history. Apart from the grave human suffering, geopolitically, the conflict constitutes an intricate and often opaque theater in which foreign actors continue to pursue their strategic objectives whilst further obfuscating an already ambiguous situation. This article aspires to shed light on the current situation and present relevant scenarios.

Adaptation strategies to climate change need information about present and future climatic conditions. However, next to scenarios about the future climate, scenarios about future vulnerability are essential, since also changing societal conditions fundamentally determine adaptation needs. At the international and national level, first initiatives for developing vulnerability scenarios and so-called shared socioeconomic pathways (SSPs) have been undertaken. Most of these scenarios, however, do not provide sufficient information for local scenarios and local climate risk management. There is an urgent need to develop scenarios for vulnerability at the local scale in order to complement climate change scenarios. Heat stress is seen as a key challenge in cities in the context of climate change and further urban growth. Based on the research project ZURES (ZURES 2020 website), the paper presents a new method for human vulnerability scenarios to heat stress at the very local scale for growing medium-sized cities. In contrast to global models that outline future scenarios mostly with a country-level resolution, we show a new method on how to develop spatially specific scenario information for different districts within cities, starting from the planned urban development and expansion. The method provides a new opportunity to explore how different urban development strategies and housing policies influence future human exposure and vulnerability. Opportunities and constraints of the approach are revealed. Finally, we discuss how these scenarios can inform future urban development and risk management strategies and how these could complement more global or national approaches.

Over the last decades, energy and pollution control policies combined with structural changes in the economy decoupled emission trends from economic growth, increasingly also in the developing world. It is found that effective implementation of the presently decided national pollution control regulations should allow further economic growth without major deterioration of ambient air quality, but will not be enough to reduce pollution levels in many world regions. A combination of ambitious policies focusing on pollution controls, energy and climate, agricultural production systems and addressing human consumption habits could drastically improve air quality throughout the world. By 2040, mean population exposure to PM2.5 from anthropogenic sources could be reduced by about 75% relative to 2015 and brought well below the WHO guideline in large areas of the world. While the implementation of the proposed technical measures is likely to be technically feasible in the future, the transformative changes of current practices will require strong political will, supported by a full appreciation of the multiple benefits. Improved air quality would avoid a large share of the current 3–9 million cases of premature deaths annually. At the same time, the measures that deliver clean air would also significantly reduce emissions of greenhouse gases and contribute to multiple UN sustainable development goals. This article is part of a discussion meeting issue ‘Air quality, past present and future’.

To understand future land use change, and related ecological and social impacts, scenario planning has become increasingly popular. We demonstrate an approach for translating scenario narratives into spatially explicit land use maps. Starting from four previously developed scenarios of land use change in southwestern Ethiopia we developed a baseline land use map, and rules for how to modify the baseline map under each scenario. We used the proximity-based scenario generator of the InVEST software to model the prospective land cover changes to existing forest (53%), arable land (26%), pasture (11%), and wetlands (7%), under the four future scenarios. The model results indicate that forest cover area would remain essentially the same under the “gain over grain” and “biosphere reserve” scenarios. Coffee plantations would cover almost half the landscape (49%) in the “mining green gold” scenario, whereas arable land would expand and cover more than half of the landscape (57%) in the “food first” scenario. The approach presented here integrates future land use mapping with participatory, narrative-based scenario research to assess the social-ecological outcomes of alternative futures. The translation of narratives onto maps can help researchers and stakeholders better understand and communicate potential land use changes, and facilitate a more spatially nuanced approach to managing or adapting to broad scale socioeconomic changes. Our study constitutes a methodological contribution to the management of land use change, as well as a tool to facilitate transparent policy negotiation and communication at local, government, and NGO levels.

Climate science provides strong evidence of the necessity of limiting global warming to 1.5 °C, in line with the Paris Climate Agreement. The IPCC 1.5 °C special report (SR1.5) presents 414 emissions scenarios modelled for the report, of which around 50 are classified as ‘1.5 °C scenarios’, with no or low temperature overshoot. These emission scenarios differ in their reliance on individual mitigation levers, including reduction of global energy demand, decarbonisation of energy production, development of land-management systems, and the pace and scale of deploying carbon dioxide removal (CDR) technologies. The reliance of 1.5 °C scenarios on these levers needs to be critically assessed in light of the potentials of the relevant technologies and roll-out plans. We use a set of five parameters to bundle and characterise the mitigation levers employed in the SR1.5 1.5 °C scenarios. For each of these levers, we draw on the literature to define ‘medium’ and ‘high’ upper bounds that delineate between their ‘reasonable’, ‘challenging’ and ‘speculative’ use by mid century. We do not find any 1.5 °C scenarios that stay within all medium upper bounds on the five mitigation levers. Scenarios most frequently ‘over use’ CDR with geological storage as a mitigation lever, whilst reductions of energy demand and carbon intensity of energy production are ‘over used’ less frequently. If we allow mitigation levers to be employed up to our high upper bounds, we are left with 22 of the SR1.5 1.5 °C scenarios with no or low overshoot. The scenarios that fulfil these criteria are characterised by greater coverage of the available mitigation levers than those scenarios that exceed at least one of the high upper bounds. When excluding the two scenarios that exceed the SR1.5 carbon budget for limiting global warming to 1.5 °C, this subset of 1.5 °C scenarios shows a range of 15–22 Gt CO 2 (16–22 Gt CO 2 interquartile range) for emissions in 2030. For the year of reaching net zero CO 2 emissions the range is 2039–2061 (2049–2057 interquartile range).

Ensembles of climate change mitigation scenarios present users with a collection of strategies for limiting global warming. These strategies may differ in their associated feasibility challenges, mitigation co-impacts, and ultimately their relative societal desirability. Understanding these scenario characteristics is therefore crucial when scenarios are used to inform strategic decisions. One approach to enhance this understanding is to establish scenario archetypes and select contrasting illustrative scenarios from a larger ensemble. We present a new multidimensional framework for the systematic comparison of scenarios at the global or regional level. We illustrate the framework with comparisons in seven dimensions: economic feasibility, mineral resource availability, impacts on societal resilience, near-term scenario robustness, environmental sustainability, interregional fairness, and speed of societal transformation. Using cluster analysis, the framework can be used to select a group of illustrative scenarios with contrasting scores across the dimensions. Beyond the selection of scenarios, our exploration and evaluation framework also allows the identification of gaps in the scenario space that may be of interest but are not covered by the literature. We demonstrate these use cases by applying our framework to a set of mitigation scenarios that limit warming to 1.5 °C. Our results show our framework systematically selects contrasting scenarios, with our illustrative pathways having diverging energy mixes and uses of carbon dioxide removal. Further, we highlight considerable regional differences in the distribution of indicator and dimension scores as a key area for further investigation.

Publicly accessible, elaborated grid datasets, i.e., benchmark grids, are well suited to publish and compare methods or study results. Similarly, developing innovative tools and algorithms in the fields of grid planning and grid operation is based on grid datasets. Therefore, a general methodology to generate benchmark datasets and its voltage level dependent implementation is described in this paper. As a result, SimBench, a comprehensive dataset for the low, medium, high and extra-high voltage level, is presented. Besides grids that can be combined across several voltage levels, the dataset offers an added value by providing time series for a whole year as well as future scenarios. In this way, SimBench is applicable for many use cases and simplifies reproducing study results. As proof, different automated algorithms for grid planning are compared to show how to apply SimBench and make use of it as a simulation benchmark.

PurposeIn prospective life cycle assessment (pLCA), inventory models represent a future state of a production system and therefore contain assumptions about future developments. Scientific quality should be ensured by using foresight methods for handling these future assumptions during inventory modelling. We present a stepwise approach for integrating future scenario development into inventory modelling for pLCA studies. MethodsA transdisciplinary research method was used to develop the SIMPL approach for scenario-based inventory modelling for pLCA. Our interdisciplinary team of LCA and future scenario experts developed a first draft of the approach. Afterwards, 112 LCA practitioners tested the approach on prospective case studies in group work projects in three courses on pLCA. Lessons learned from application difficulties, misunderstandings and feedback were used to adapt the approach after each course. After the third course, reflection, discussion and in-depth application to case studies were used to solve the remaining problems of the approach. Ongoing courses and this article are intended to bring the approach into a broader application.Results and discussionThe SIMPL approach comprises adaptations and additions to the LCA goal and scope phase necessary for prospective inventory modelling, particularly the prospective definition of scope items in reference to a time horizon. Moreover, three iterative steps for combined inventory modelling and scenario development are incorporated into the inventory phase. Step A covers the identification of relevant inventory parameters and key factors, as well as their interrelations. In step B, future assumptions are made, by either adopting them from existing scenarios or deriving them from the available information, in particular by integrating expert and stakeholder knowledge. Step C addresses the combination of assumptions into consistent scenarios using cross-consistency assessment and distinctness-based selection. Several iterations of steps A–C deliver the final inventory models.ConclusionThe presented approach enables pLCA practitioners to systematically integrate future scenario development into inventory modelling. It helps organize possible future developments of a technology, product or service system, also with regard to future developments in the social, economic and technical environment of the technology. Its application helps to overcome implicit bias and ensures that the resulting assessments are consistent, transparently documented and useful for drawing practically relevant conclusions. The approach is also readily applicable by LCA practitioners and covers all steps of prospective inventory modelling.

The start of the growing season for alpine plants is primarily determined by the date of snowmelt. We analysed time series of snow depth at 23 manually operated and 15 automatic (IMIS) stations between 1055 and 2555 m asl in the Swiss Central Alps. Between 1958 and 2019, snowmelt dates occurred 2.8 ± 1.3 days earlier in the year per decade, with a strong shift towards earlier snowmelt dates during the late 1980s and early 1990s, but non-significant trends thereafter. Snowmelt dates at high-elevation automatic stations strongly correlated with snowmelt dates at lower-elevation manual stations. At all elevations, snowmelt dates strongly depended on spring air temperatures. More specifically, 44% of the variance in snowmelt dates was explained by the first day when a three-week running mean of daily air temperatures passed a 5 °C threshold. The mean winter snow depth accounted for 30% of the variance. We adopted the effects of air temperature and snowpack height to Swiss climate change scenarios to explore likely snowmelt trends throughout the twenty-first century. Under a high-emission scenario (RCP8.5), we simulated snowmelt dates to advance by 6 days per decade by the end of the century. By then, snowmelt dates could occur one month earlier than during the reference periods (1990–2019 and 2000–2019). Such early snowmelt may extend the alpine growing season by one third of its current duration while exposing alpine plants to shorter daylengths and adding a higher risk of freezing damage.