ABSTRACT
Our plans to tackle climate change could be thrown off-track by shocks such as the coronavirus pandemic, the energy supply crisis driven by the Russian invasion of Ukraine, financial crises and other such disruptions. We should therefore identify plans which are as resilient as possible to future risks, by systematically understanding the range of risks to which mitigation plans are vulnerable and how best to reduce such vulnerabilities. Here, we use electricity system decarbonization as a focus area, to highlight the different types of technological solutions, the different risks that may be associated with them, and the approaches, situated in a decision-making under deep uncertainty (DMDU) paradigm, that would allow the identification and enhanced resilience of mitigation pathways. Copyright © 2023 Gambhir and Lempert.
ABSTRACT
The Olympic Games continue to be a creator of adverse environmental impacts for host communities. Given the role that the Olympic Games play in sustainability due to their size, the number of people attending, new construction and infrastructure, and the extensive exposure by the media, this study investigated the Tokyo 2020 Games by evaluating the efficacy of their ecological sustainability efforts. Methods for this study were framed by the conceptual model of Muller et al. Specifically, the model is grounded on the three general aspects of sustainability: ecological, social, and economic. Compared to all Olympic events from 1992 through 2020, results from the present research indicated that Tokyo 2020 Olympics may have been the most ecologically friendly Games. This ecological record is significant, but it may be an unrealistic benchmark, given that the lack of attendance due to the COVID-19 pandemic influenced much of the ecological sustainability scores.
ABSTRACT
At present, the novel coronavirus variant strain has broken out in some cities in China. After prevention and control, the epidemic situation is basically in the final stage of control. To assess the resilience of cities in different regions, this paper established a COVID-19 resilience assessment model based on TOPSIS and fuzzy mathematics. Firstly, according to the concept of urban resilience, three first-level indicators are selected, namely elasticity, resilience and adaptation. Then 17 second-level indicators are selected to quantify the three first-level indicators. Finally, based on the results of the model, possible approaches to improve resilience in COVID-19 prevention and control are proposed. © 2022 SPIE.
ABSTRACT
Part of the economic recovery plans implemented by governments following COVID-19 is directed towards the energy transition. To understand the potential effects of these post-COVID green recovery packages on reductions of global greenhouse gas emissions until 2030, we investigated three different approaches. First, we analyzed simulation results of Integrated Assessment Models (IAMs) to infer the change in CO2 intensity of GDP that could result from post-COVID low-carbon investment plans. Second, we investigated the scenarios the International Energy Agency (IEA) provided based on a bottom-up energy system model. Combining the two approaches, we found that green recovery packages implemented and planned globally can lead to an emissions reduction of merely 1%-6% from the 2030 baseline levels at most. Third, we looked into the results of the Adaptative Regional Input-Output model, which simulates the dynamic effects of economic crisis and fiscal stimuli through supply chains following labor shortage. The third approach shows that the increase of activity driven by fiscal stimuli leads to a rebound of CO2 emissions even if they do not target carbon-intensive sectors. We conclude that green recovery packages targeting low-carbon technologies have a limited impact on near-term CO2 emissions and that demand-side incentives, as well as other policy efforts to disincentivize the use of fossil fuels, are also crucial for scaling up climate mitigation.
ABSTRACT
It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here, we provide complementary perspectives to reiterate that substantial, broad, and sustained policy efforts beyond stimulus packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from integrated assessment models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.
ABSTRACT
Supporting investments in energy efficiency is considered a robust strategy to achieve a successful transition to low-carbon energy systems in line with the Paris Agreement. Increased energy efficiency levels are expected to reduce the need for supply-side investments in controversial technologies, such as carbon dioxide capture and storage (CCS) and nuclear energy, and to induce a downward push on carbon prices, which may facilitate the political and societal acceptance of climate policies, without adversely affecting living comfort and sustainable development. In order to fully reap these potential benefits, economies need to design policy packages that balance emission reduction incentives on both the demand and the supply side. In this paper we carry out a model-comparison exercise, using two well-established global integrated assessment models, PROMETHEUS and TIAM-ECN, to quantitatively analyze the global system-level effects of increased energy efficiency in the context of ambitious post-COVID climate change mitigation scenarios. Our results confirm the expected benefits induced by higher energy efficiency levels, as in 2050 global carbon prices are found to decline by 10%–50% and CO2 storage from CCS plants is 13%–90% lower relative to the “default” mitigation scenarios. Similarly, enhanced energy efficiency reduces the additional average yearly system costs needed globally in 2050 to achieve emission reductions in line with the Paris Agreement. These additional costs are estimated to be of the order of 2 trillion US$ – or 1% of global GDP – in a well-below-2 °C scenario, and can be reduced by 6–30% with the adoption of higher energy efficiency standards. While the two models project broadly consistent future trends for the energy mix in the various scenarios, the effects may differ in magnitude due to intrinsic differences in how the models are set up and how sensitive they are to changes in energy efficiency and emission reduction targets. © 2022
ABSTRACT
In the field of assessment in higher education, practitioners regularly mention two features as key for success at implementing a sustainable assessment model, i) it has to be faculty-driven, and ii) it has to become part of the curriculum. This paper describes an assessment model incorporating these features. The model was developed and implemented for assessing both the ETAC-ABET Program Criteria for an associate degree in Electromechanical Engineering Technology program and the new ETAC-ABET (2019-2020) student outcome (2), which regards students' ability to design systems, components, or processes for well-defined engineering technology problems appropriate to the discipline. This paper describes the implementation of our assessment model, including a description of the performance indicators used for assessment of criteria mentioned above, the structure of course selected for the assessment, the details of the laboratory experiments and final design project used as assessment tools, a summary of the collected data and a discussion of the assessment results, and the arrangements we made in response to the COVID-19 pandemic. We believe that our method of incorporating assessment as regular course activities helps achieve the ultimate objective of education, i.e., continuous and constant improvement of students' competencies and learning experiences. © American Society for Engineering Education, 2021
ABSTRACT
OBJECTIVES: To investigate the association of risk of venous thromboembolism with 30-day mortality in COVID-19 patients. METHODS: A total of 1030 COVID-19 patients were retrospectively collected, with baseline data on demographics, sequential organ failure assessment (SOFA) score, and VTE risk assessment models (RAMs), including Padua prediction score (PPS), International Medical Prevention Registry (IMPROVE), and Caprini. RESULTS: Thirty-day mortality increased progressively from 2% in patients at low VTE risk to 63% in those at high risk defined by PPS. Similar findings were observed in IMPROVE and Caprini scores. Progressive increases in VTE risk were also associated with higher SOFA score. High risk of VTE was independently associated with mortality regardless of adjusted gender, smoking status and some comorbidities, with hazard ratios of 29.19, 37.37 and 20.60 for PPS, IMPROVE and Caprini RAM, respectively (P < 0.001 for all comparisons). The predictive accuracy of PPS (area under curve (AUC) 0.900), IMPROVE (AUC 0.917), or Caprini (AUC 0.861) RAM for risk of hospitalized mortality was unexpectedly strong. CONCLUSIONS: We established that the presence of a high risk of VTE identifies a group of COVID-19 patients at higher risk for mortality. Furthermore, there is a high accuracy of VTE RAMs to predict mortality in these patients.