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Introduction Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a therapeutic method used for decades in neurological diseases such as Parkinson’s disease (PD), Huntington’s disease (HD) or dystonia. HD is a rare, inherited, neurodegenerative condition that causes progressive motor deficits, psychiatric symptoms, and cognitive impairment. Objectives Moreover, after DBS as a psychiatric side effect has been marked and the etiology of that side effect is not well- understood. Methods A case study of a 51 years old male is presented, who developed involuntary movements, for the first time at the age of 17, being diagnosed with Chorea Huntington, was treated with medication without improvement of the symptoms, such as rigidity and bradykinesia. After ten years, based on guidelines, he was treated with DBS, the outcome of which showed complete improvement of neurological symptomatology. Nevertheless, he started to present delusional ideas of reference with his siblings, sleep disturbance, dysphoria and agitation. Results Obviously, DBS improved neurological symptomatology permanently. The medical history of our patient has shown the recurrence of psychiatric symptoms as a few mandatory psychiatric hospitalizations and his condition has improved with olanzapine 20 mg/ daily and L.A.I. of paliperidone (once /monthly). Conclusions By far, DBS, as a treatment modality, has great potential to modify disease outcomes and potentially cure the devastating genetic neurodegenerative disorder such as chorea. The cases with psychiatric side effects of DBS have been described so rarely, that it’s difficult to formulate conclusions that can be applied to the whole population of patients treated with DBS. In our opinion, in some cases it is possible to effectively treat the psychotic symptoms without resignation from the benefits of DBS. Disclosure of Interest None Declared

Despite the significant volume of fiscal recovery measures announced by countries to deal with the COVID-19 crisis, most recovery plans allocate a low percentage to green recovery. We present scenarios exploring the medium- and long-term impact of the COVID-19 crisis and develop a Green Recovery scenario using three well-established global models to analyze the impact of a low-carbon focused stimulus. The results show that a Green Recovery scenario, with 1% of global GDP in fiscal support directed to mitigation measures for 3 years, could reduce global CO2 emissions by 10.5–15.5% below pre-COVID-19 projections by 2030, closing 8–11.5% of the emissions gap with cost-optimal 2°C pathways. The share of renewables in global electricity generation is projected to reach 45% in 2030, the uptake of electric vehicles would be accelerated, and energy efficiency in the buildings and industry sector would improve. However, such a temporary investment should be reinforced with sustained climate policies after 2023 to put the world on a 2°C pathway by mid-century.

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

The EU decarbonization strategy foresees deep cuts in CO2 in the transport sector. Investment in infrastructure, manufacturing of new technology vehicles and production of alternative fuels induce macroeconomic changes in activity and employment for both national and regional economies. The objective of the paper is to present a newly built macroeconomic-regional model (GEM-E3-R general equilibrium model for economy, energy and environment for regions) for assessing impacts of transport sector restructuring on regional economies of the entire EU, segmented following NUTS-3 (nomenclature of territorial units of statistics). The model combines general economic equilibrium theory with location choice and New Economic Geography and implements a dynamic, fully endogenous agglomeration-dispersion mechanism for people and industries coupled with a gravity model for bilateral interregional flows. A novelty of the model is a two-layers structure: (i) the country-wide layer formulated as a global multi-sector, multi-country and multi-period computable general equilibrium (CGE) model; and (ii) the regional economy layer, which simulates impacts on regional economies, while considering country-wide economic trends as boundary conditions. The paper presents a use of the model in the assessment of regional economic effects of electrification of car mobility in Europe and wide use of domestically produced advanced biofuels.