Perceptions of environmental risks in Mozambique : implications for the success of adaptation and coping strategies

Policies to promote adaptation climate risks often rely on the willing cooperation of the intended beneficiaries. If these beneficiaries disagree with policy makers and programme managers about the need for adaptation, or the effectiveness of the measures they are being asked to undertake, then implementation of the policies will fail. A case study of a resettlement programme in Mozambique shows this to be the case. Farmers and policy-maker disagreed about the seriousness of climate risks, and the potential negative consequences of proposed adaptive measures. A project to provide more information about climate change to farmers did not change their beliefs. The results highlight the need for active dialog across stakeholder groups, as a necessary condition for formulating policies that can then be successfully implemented.Hazard Risk Management,Environmental Economics&Policies,Climate Change,Population Policies,Rural Poverty Reduction

Active machine learning for spatio-temporal predictions using feature embedding

Active learning (AL) could contribute to solving critical environmental problems through improved spatio-temporal predictions. Yet such predictions involve high-dimensional feature spaces with mixed data types and missing data, which existing methods have difficulties dealing with. Here, we propose a novel batch AL method that fills this gap. We encode and cluster features of candidate data points, and query the best data based on the distance of embedded features to their cluster centers. We introduce a new metric of informativeness that we call embedding entropy and a general class of neural networks that we call embedding networks for using it. Empirical tests on forecasting electricity demand show a simultaneous reduction in prediction error by up to 63-88% and data usage by up to 50-69% compared to passive learning (PL) benchmarks

What are the factors and needs promoting mobility-as-a-service? Findings from the Swiss Household Energy Demand Survey (SHEDS)

Mobility-as-a-Service (MaaS) is a service that supports customers' transportation needs by providing information and ticketing for a multitude of transport modes in one interface; thus, buy potentially fostering multimodality and public transport, it represents an important lever to reduce negative transportation impacts such as emissions and congestion. By means of an online survey conducted in Switzerland, we try to understand potential user needs as well as factors that would motivate the use of MaaS. Comparing the openness to use MaaS for specific trip purposes like commuting and leisure activities, we find the lowest level of openness for commuting and the highest for weekend leisure trips. Intention to reduce car usage was positively related to openness to MaaS in commuting. On the other hand, factors that positively influence openness to using MaaS for leisure activities include a higher education degree, experience with carsharing and the use of transport-related climate policy announcements directly affecting consumers. These findings suggest focusing specifically on either commuting or leisure activities when designing policy measures

Unified machine learning tasks and datasets for enhancing renewable energy

Multi-tasking machine learning (ML) models exhibit prediction abilities in domains with little to no training data available (few-shot and zero-shot learning). Over-parameterized ML models are further capable of zero-loss training and near-optimal generalization performance. An open research question is, how these novel paradigms contribute to solving tasks related to enhancing the renewable energy transition and mitigating climate change. A collection of unified ML tasks and datasets from this domain can largely facilitate the development and empirical testing of such models, but is currently missing. Here, we introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17 datasets from six different application domains related to enhancing renewable energy, including out-of-distribution validation and testing data. We unify all tasks and datasets, such that they can be solved using a single multi-tasking ML model. We further analyse the dimensions of each dataset; investigate what they require for designing over-parameterized models; introduce a set of dataset scores that describe important properties of each task and dataset; and provide performance benchmarks

Vulnerability of solar energy infrastructure and output to climate change

This paper reviews the potential vulnerability of solar energy systems to future extreme event risks as a consequence of climate change. We describe the three main technologies likely to be used to harness sunlight—thermal heating, photovoltaic (PV), and concentrating solar power (CSP)—and identify critical climate vulnerabilities for each one. We then compare these vulnerabilities with assessments of future changes in mean conditions and extreme event risk levels. We do not identify any vulnerabilities severe enough to halt development of any of the technologies mentioned, although we do find a potential value in exploring options for making PV cells more heat-resilient and for improving the design of cooling systems for CS

Insurance, public assistance and household flood risk reduction: a comparative study of Austria, England and Romania

In light of increasing losses from floods many researchers and policy makers are looking for ways to encourage flood risk reduction among communities, business, and households. In this study we investigate risk reduction behavior at the household level in three European Union (EU) Member States with fundamentally different insurance and compensation schemes. We try to understand if and how insurance and public assistance influence private risk reduction behavior. Data was collected using a telephone survey (n=1,849) of household decision makers in flood-prone areas. We show that insurance overall is positively associated with private risk reduction behavior. Warranties, premium discounts, and information provision with respect to risk reduction may be an explanation for this positive relationship in the case of structural measures. Public incentives for risk-reduction measures by means of financial and in-kind support, and particularly through the provision of information are also associated with enhancing risk reduction. In this study public compensation is not negatively associated with private risk reduction behavior. This does not disprove such a relationship, but the negative effect may be mitigated by factors related to respondent’s capacity to implement measures or social norms that were not included in the analysis. The data suggests that large-scale flood protection infrastructure creates a sense of security that is associated with a lower level of preparedness. Across the board there is ample room to improve both public and private policies to provide effective incentives for household level risk reduction

Carsharing experience fostering sustainable car purchasing? : investigating car size and powertrain choice

Scholars suggest that carsharing may lead to a reduction in car ownership and car travel. Research on how carsharing is connected to other sustainable effects such as an increased openness to micro to mid-sized battery electric vehicles is limited, however. We thus adopted a stated choice survey with 995 participants from Switzerland to test the car purchase preference of mobility users with and without carsharing experience. Results suggest that - for people living in the countryside - carsharing users have a 3 times higher likelihood of choosing a micro to mid-sized battery electric vehicle compared to participants without carsharing experience. We find a similar trend for people living in the agglomerations. We therefore recommend policy makers and mobility planners to take these benefits into account when planning carsharing services and its role in mobility systems