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The use of scenarios and quantitative modelling to identify pathways for energy system transformations in line with the Paris targets is well established in the field of energy and climate policy. The resulting decarbonization pathways depend on both assumptions and the type of model used (e.g., integrated assessment models, energy system, macro-econometric or bottom-up sector models). The objective of this article is to analyze how energy demand sectors in system-wide net-zero scenarios for the EU compare to the results of sector-specific models. To this end, a novel approach referred to as "sectoral benchmarking" is developed and applied, combining the application of standard indicators such as energy intensity, electrification rate or carbon intensity with an index decomposition analysis. The combined approach allows visualizing how system-wide decarbonization pathways differ from the sector models' pathways by bringing the model output in a harmonized format for an efficient comparison. The analysis compares pathways from four different modelling tools: two European system models, one of which is an energy system model (EU TIMES) and the other a macro-econometric model (NEMESIS); as well as two sector-specific models, for transport (ALADIN) and for the industry and building sectors (FORECAST). We evaluate the system model's net-zero scenarios by comparing them to a corridor given by the sector models' current policy and net-zero emission scenarios. This corridor represents what the sector models deem as plausible from their bottom-up perspective within the boundaries of current policies and ambitions to reach net-zero. Our results show that the system model net-zero pathways differ substantially from the sectoral perspective in all sectors. In the industry and building sectors, both system models' decarbonization ambitions are within the sector corridor, but the employed mitigation levers differ. In the industry sectors, the sectoral model achieves substantial CO 2 emission reductions with electrification, while the system models use more bioenergy (EU TIMES) or more energy efficiency (NEMESIS). In the building sector, both system models rely mostly on electrification, while the sector models relies on biomass and some district heat and electrification. In the transport sector, both system models' decarbonization ambition is substantially lower than the sector model's. The observed differences are caused by a variety of factors, which we evaluate in this article. One reason is the system models' lower ambition to decarbonize the end-use sectors due to their ability to compensate with negative emission technologies across sectors. In addition, employed mitigation levers differ due to the models' differing capabilities to consider technologies as well as differences in the allocation of bioenergy to sectors. Our findings can be used to determine how the different types of models can inform each other and to make the diverging decarbonization pathways more transparent to policy-makers and other relevant stakeholders. • System-wide EU net-zero scenarios are compared to sector model decarbonization. • System-wide scenarios use negative emissions instead of ambitious end-use levers. • A novel approach to assess sectoral dynamics in system-wide scenarios is presented. • Transport sector model more ambitious than system-wide net zero scenarios. • System-wide scenarios mostly within sector model range for industry and buildings. [ABSTRACT FROM AUTHOR]

In the EU, COVID-19 and associated policy responses led to economy-wide disruptions and shifts in services demand, with considerable energy-system implications. The European Commission's response paved the way towards enhancing climate ambition through the European Green Deal. Understanding the interactions among environmental, social, and economic dimensions in climate action post-COVID thus emerged as a key challenge. This study disaggregates the implications of climate ambition, speed of economic recovery from COVID-19, and behavioural changes due to pandemic-related measures and/or environmental concerns for EU transition dynamics, over the next decade. It soft-links two large-scale energy-economy models, EU-TIMES and NEMESIS, to shed light on opportunities and challenges related to delivering on the EU's 2030 climate targets. Results indicate that half the effort required to reach the updated 55% emissions reduction target should come from electricity decarbonisation, followed by transport. Alongside a post-COVID return to normal, the European Green Deal may lead to increased carbon prices and fossil-fuel rebounds, but these risks may be mitigated by certain behavioural changes, gains from which in transport energy use would outweigh associated consumption increases in the residential sector. Finally, the EU recovery mechanism could deliver about half the required investments needed to deliver on the 2030 ambition. • This study soft-links an energy system model and a macroeconometric model for the EU. • Electricity decarbonisation can make up half the effort to −55% followed by transport. • Investments as part of EU green recovery could finance about half of what is required. • The pandemic is unlikely to heavily impact the EU's long-term emissions trajectory. • Work- and travel-related behaviour changes could considerably reduce investment needs. [ABSTRACT FROM AUTHOR]

Introduction: Despite the demonstrated benefit of methadone, the incidence opioid-related overdose, and its associated mortality continues to rise at an alarming rate. The impact of high prevalence comorbid features such as chronic liver disease (CLD) on methadone treatment response remain unclear.Aim: To determine whether CLD is associated with poor response to methadone treatment.Methods: Using a well-established multi-center cohort from the Genetics of Opioid Addiction Study (GENOA), we evaluated if presence of CLD among 1234 eligible patients with opioid use disorder receiving methadone treatment impacted health and behavioural responses to treatment. CLD was classified as any liver disorder/dysfunction present for a minimum period of six months. Serial urine toxicology assessments were used to determine treatment response. The effect of CLD was determined using a multi-variable logistic regression model.Results: CLD was present in 25 % (n = 314) of the population. On average, patients with CLD were found to be older (mean age 44 vs 36 years, p < 0.0001), unemployed (81.8 % vs 61 %, p < 0.0001), and receiving government disability benefits at significantly higher rates (21.9 % vs 11 %, p < 0.0001). Increased levels of physical craving, emotional stress, as well as health risk behaviors were noted in CLD patients. Findings from the multi-variable model demonstrate a 68 % increased risk for dangerous opioid consumption behaviors (Odds Ration [OR]: 1.68, 95 % Confidence Interval [CI] 1.22, 2.31, p = 0.001) among patients with CLD. Methadone dose (OR: 0.76, 95 % CI 0.70, 0.81, p < 0.0001) was shown to be protective with a significant risk reduction of 24 % per 20 mg increase in methadone. Duration in treatment was also found to be protective (OR: 0.99, 95 % CI 0.97, 0.99, p < 0.0001).Conclusion: CLD poses a distinct risk for patients with opioid addiction. Closer drug monitoring, and substance use contingency management should be considered to reduce mortality risk in these patients. [ABSTRACT FROM AUTHOR]