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Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

AbstractClimate change impacts are being felt across sectors in all regions of the world, and adaptation projects are being implemented to reduce climate risks and existing vulnerabilities. Climate adaptation actions also have significant synergies and tradeoffs with the Sustainable Development Goals (SDGs), including SDG 5 on gender equality. Questions are increasingly being raised about the gendered and climate justice implications of different adaptation options. This paper investigates if reported climate change adaptation actions are contributing to advancing the goal of gender equality (SDG 5) or not. It focuses on linkages between individual targets of SDG 5 and climate change adaptation actions for nine major sectors where transformative climate actions are envisaged. The assessment is based on evidence of adaptation actions documented in 319 relevant research publications published during 2014–2020. Positive links to nine targets under SDG 5 are found in adaptation actions that are consciously designed to advance gender equality. However, in four sectors—ocean and coastal ecosystems; mountain ecosystems; poverty, livelihood, sustainable development; and industrial system transitions, we find more negative links than positive links. For adaptation actions to have positive impacts on gender equality, gender-focused targets must be intentionally brought in at the prioritisation, designing, planning, and implementation stages. An SDG 5+ approach, which takes into consideration intersectionality and gender aspects beyond women alone, can help adaptation actions move towards meeting gender equality and other climate justice goals. This reflexive approach is especially critical now, as we approach the mid-point in the timeline for achieving the SDGs.

The cluster on Enabling Technologies in the framework of the Fast Track on Energy Communities workshop held in Rome during the Sustainable Places 2021 conference presented a series of H2020 projects and their innovative technological solutions to facilitate the uptake of energy communities, demand-response and energy efficiency projects in Europe.

According to Eurostat, Italy is the fifth country of the European Union per immigrant population. The complexity of the phenomenon, as it has evolved in recent years, leads to analyzing it from a specific point of view, that of the real estate market. The article represents the early stage of research on the housing condition of the immigrant population in the Southern Italy and its effect on the housing market. First, we describe the spatial segregation phenomenon affecting the immigrant population in Campania Region; then we analyze data of the municipality of Castel Volturno, which has one of the greater migratory pressure throughout the whole region. We provide statistical regressions correlating housing prices and socio-economic features from 2006 to 2016. The results confirm the findings of the current literature on the subject: there is a specific phenomenon associated with the presence of an immigrant population residing in conjunction with a reduction of housing prices.