18 research outputs found
Context-Specific Energy Strategies: Coupling Energy System Visions with Feasible Implementation Scenarios
Conventional energy strategy defines an energy system
vision (the
goal), energy scenarios with technical choices and an implementation
mechanism (such as economic incentives). Due to the lead of a generic
vision, when applied in a specific regional context, such a strategy
can deviate from the optimal one with, for instance, the lowest environmental
impacts. This paper proposes an approach for developing energy strategies
by simultaneously, rather than sequentially, combining multiple energy
system visions and technically feasible, cost-effective energy scenarios
that meet environmental constraints at a given place. The approach
is illustrated by developing a residential heat supply strategy for
a Swiss region. In the analyzed case, urban municipalities should
focus on reducing heat demand, and rural municipalities should focus
on harvesting local energy sources, primarily wood. Solar thermal
units are cost-competitive in all municipalities, and their deployment
should be fostered by information campaigns. Heat pumps and building
refurbishment are not competitive; thus, economic incentives are essential,
especially for urban municipalities. In rural municipalities, wood
is cost-competitive, and community-based initiatives are likely to
be most successful. Thus, the paper shows that energy strategies should
be spatially differentiated. The suggested approach can be transferred
to other regions and spatial scales
Additional file 1 of Association between the introduction of a national targeted intervention program and the incidence of surgical site infections in Swiss acute care hospitals
Additional file 1. Supplementary Information
Hazard ratios with 95% confidence intervals from separate univariable Cox regression models for each predictor involved in the multivariable models regarding COVID-19 risk by period (Table 1 in main text).
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Definition of four groups by immune status and outcomes.
Created with BioRender.com. (TIFF)</p
Rate ratio (RR) and 95% confidence intervals (CI) from multivariable Poisson regression regarding number of symptoms reported from SARS-CoV-2 infections during the Omicron period.
Model includes booster vaccine and is therefore restricted to groups V and H. (PDF)</p
Flow sheet of participants in the SURPRISE study showing reasons (and respective number of participants) for exclusion from current analysis as well as participants within each immune status including number of subsequently vaccinated individuals, respectively.
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Sensitivity analyses: Comparison of hazard ratios obtained in Cox models with three independent missing value imputations, without missing value imputation (i.e., complete case analysis), and with exclusion of events occurring during the period of variant overlap between December 6, 2021 and January 3, 2022.
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