What can we learn from the 2008 financial crisis for global power decarbonization after COVID-19?

Since the outbreak of the COVID-19 pandemic, power generation and the associated CO2 emissions in major countries have experienced a decline and rebound. Knowledge on how an economic crisis affects the emission dynamics of the power sector would help alleviate the emission rebound in the post-COVID-19 era. In this study, we investigate the mechanism by which the 2008 global financial crisis sways the dynamics of power decarbonization. The method couples the logarithmic mean Divisia index (LMDI) and environmentally extended input-output analysis. Results show that, from 2009 to 2011, global power generation increased rapidly at a rate higher than that of GDP, and the related CO2 emissions and the emission intensity of global electricity supply also rebounded;the rapid economic growth in fossil power-dominated countries (e.g., China, the United States, and India) was the main reason for the growth of electricity related CO2 emissions;and the fixed capital formation was identified as the major driver of the rebound in global electricity consumption. Lessons from the 2008 financial crisis can provide insights for achieving a low-carbon recovery after the COVID-19 crisis, and specific measures have been proposed, for example, setting electricity consumption standards for infrastructure construction projects to reduce electricity consumption induced by the fixed capital formation, and attaching energy efficiency labels and carbon footprint labels to metal products (e.g., iron and steel, aluminum, and fabricated metal products), large quantities of which are used for fixed capital formation. © 2023 The Authors

Understanding how policy settings affect developer decisions

Key points Private sector residential development is driven by profit. Developers want policy certainty so they can factor these policy settings into their assessment of the potential financial feasibility of a development site. The complexity of the development process, the structure of development organisations, the variety of products delivered, and land ownership issues mean the development decision-making process varies by organisation and site by site. Therefore, it is too simplistic to assume policy settings will have exactly the same impact on each and every developer and on each and every site. Housing market conditions drive private sector development. Policies that stimulate or restrict market demand will impact levels of housing supply. Once a developer has purchased land for development, any new costs introduced through regulation will impact profitability. Developers will try and pass these costs onto consumers through higher prices in order to maintain profit but their ability to do so will depend on market conditions. Reducing development costs will not automatically result in a more affordable end product. Such cost reductions could end up in a higher price paid for the land, additional profits for the developer or a combination. Reducing development approval timelines has a positive impact on profitability outcomes. New construction technologies that reduce development timelines can also have a positive impact on profitability outcomes. Affordable housing contributions required from a development site need to be known by a developer well in advance of land purchase so they can be factored into assessments of profitability and land price. Mandatory affordable housing contributions are the most likely source of large-scale affordable housing contributions in Australia and many sites would be able to absorb the costs of such delivery under a well-designed, efficient and consistent policy. Key findings This research examined how policy settings affect developer decisions, necessary to provide policy makers with an understanding of how private sector housing supply is likely to react to settings and events which affect development costs, revenues and timeframes. The research also examines the issue of new construction technologies and processes to establish their potential for reducing development costs and timelines, improving affordability. The development industry is incredibly complex, made up of hundreds of different organisations with a myriad of different structures. As such, a project like this can only take a broad-brush approach to highlight the impact of a range of settings on traditional approaches to development. The general findings show the importance of market conditions in driving supply and how factors such as infrastructure costs, delays in development approvals and construction timelines have a negative impact on profit outcomes if they cannot be factored into a developer's initial assessment of site profitability. Such assessments are conducted through a discounted cash-flow based feasibility modelling process. Factors that are certain to the developer, such as prevailing taxes and construction costs, can be carefully considered in a decision to develop, it is the unknowns that developers fear. Clear and consistent policy settings, certainty in development timeframes and certainty in policy advice create the ideal conditions for development. The rest is dependent on market conditions. Private sector development is driven by profit, specifically the balance between risk and return. There are a number of factors that affect this balance: Market conditions: strong demand, rising prices, cheap and accessible credit and high levels of consumer confidence are the perfect conditions for developers. Unexpected price rises during a development period will result in higher than anticipated profits for a developer. Risk: there are a number of factors developers consider when assessing risk and the level of return required to compensate for that risk. Market conditions is one, with the other being related to costs and timelines that a developer in unable to fully predict in their feasibility modelling. Those settings are fixed and easy to predict, and therefore assess. If costs are too high for the level of return required from a given site, the site will not be considered profitable and no development will occur. It is those factors that may vary after a developer has set the development process in motion that are problematic. Unforeseen delays caused by development approval processes, unexpected infrastructure costs, new taxes, labour and/or supply shortages, weather events and changes to design requirements can mean an increase in costs or a decrease in revenue and result in a lower than expected return. Landowner expectations: landowners will often be well aware of prevailing market values in an area and will engage consultants who will adopt feasibility modelling processes to come up with what they believe is a realistic price. Add additional costs into the development process and a developer may not be able to meet the landowner's price expectations, preventing development on a site until the landowner lowers their expectations, a developer is somehow able to reduce costs or predicted revenues rise. The current model of determining land purchase price benefits the landowner rather than the developer as the landowner benefits from the uplift in value associated with re-zoning and development approval. Reducing the land cost input could enable a developer to deliver a dwelling product to the market at a lower price. However the prices of new dwellings are generally set with reference to the comparable products in the local market, meaning there is little incentive for the developer to price below market other than to increase sales rates. Cost certainty and price setting: the costs of development will be factored into the price a developer pays for land. Unexpected cost increases post land purchase will need to be either absorbed by the developer in the form of lower profits, or passed onto the end consumer to maintain predicted profit levels. In some cases, developments will be profitable enough to absorb unexpected increases, but in a competitive development industry these are the exception rather than the norm. While the development industry often states that increased costs will end in higher prices for the end consumer, the ability of developers to pass on these costs depends on market conditions. Prices are determined in the local market and unless the developer has created an entirely new market with no local competition, that market will determine prices, i.e. how much consumers are prepared to pay. In a market with strong local supply and weak local demand, a developer will be lucky to maintain prices predicted at the start of the development process let alone increase prices to absorb costs. This research conducted feasibility modelling to examine how changes to key input variables affect development return outcomes. The modelling was based on a developer purchasing the land upfront with their own equity. While there are many other models of the development process and the timing of land purchase, this is considered the most common. The modelling outputs can be summarised as follows: Small increases or reductions in end sales prices have major implications for Internal Rate of Return (IRR) outcomes. A 10 per cent fall in revenue can mean a 50 per cent drop in the IRR. This means end sale prices are the biggest risk factor in the development process. Small increases or reductions in direct costs of construction can also have major implications for returns. A 10 per cent increase in costs can mean a 40 per cent reduction in the IRR. Significant reductions in the time taken from the commencement to completion of construction can have a positive impact on feasibility. Even a one or two month reduction in a 24 month build time can mean the difference between a profitable and unprofitable development. An increase in the time taken for development approval after land purchase will have a modest, ne ative impact on return outcomes. The longer the delay, the greater the impact. Policy development options Stimulating the market The Australian Government's response to COVID-19 through HomeBuilder and associated state government grants showed how quickly demand side incentives can stimulate the housing industry and deliver new housing supply. Grants increase a consumer's capacity to buy while also increasing confidence in the market. This reduces risk for the developer, which along with higher prices, stimulates the development of new sites. If governments are to use such spending to stimulate housing markets in the future, they must learn from HomeBuilder and how sharp increases in supply puts pressure on the building industry through labour shortages and material price increases. Smoothing housing supply over a longer period rather than a HomeBuilder-like rush would help the industry cope and avoid capacity constraints. A stimulus scheme operating across the entire industry, rather than concentrating on detached homebuilding, would also be more equitable. But what HomeBuilder has shown is demand side grants are an effective way of boosting housing supply in the short term and bringing forward development activity reliant on greater certainty and improved market conditions. Inclusionary zoning The introduction of affordable housing contribution requirements through inclusionary zoning can have a major impact on development feasibility. While density and height bonuses can help replace revenue, the developer needs to be able to pass costs onto the landowner and that means knowing well in advance what such requirements are likely to be. It will then be up to the landowner to determine if the resulting land price is sufficient to stimulate a sale. © AustralianHousing and Urban Research Institute Limited 2022.

Farmer Households' Livelihood Resilience in Ethnic Tourism Villages: A Case Study of the Wuling Mountain Area, China

Farmer households in tourist villages have been severely impacted by the COVID-19 pandemic, and the recovery of livelihood is proving difficult. In order to improve farmer households' ability to cope with external shocks, we have applied the theoretical framework of resilience to study farmer households' livelihood in ethnic tourism villages. Based on the survey data of 480 farmer households from 10 ethnic tourism villages in the Wuling Mountain area, this study constructs a livelihood resilience evaluation index system from three of the following dimensions: buffer capacity, adaptive capacity, and transformation capacity. These households are classified into three types: government-led, company-led, and community-led. In addition, the livelihood resilience and its influencing factors of each type is quantitatively assessed. The results show that the livelihood resilience of farmer households administered by the government, companies, and communities is 0.2984, 0.3250, and 0.2442, respectively. Government-led farmer households have the greatest transformation capacity, company-led farmer households have the largest buffer capacity and adaptive capacity, and community-led farmer households have the least capacity across the board. The results indicated that the company-led management of tourism development is currently the most appropriate mode of management for the local area. Four factors, namely, the number of family members engaged in tourism, the training opportunities for the development of professional skills, the education level of core family members, and the type of assistance subsidy available to a family, are the dominant obstacle factors with respect to the livelihood resilience of different types of farmer households. Finally, some recommendations are made to improve the farmer households' livelihood resilience in ethnic tourism villages based on two aspects of organization management and farmer households' behavior. The findings of this study can be used as a theoretical foundation for future research on farmer households' resilience to poverty in underdeveloped ethnic tourism villages. © 2022 by the authors.

Complex Systems of Disaster Response: The Case of COVID-19

At present, disasters frequently occur throughout the world. Due to different cultural backgrounds and organisational structures, most countries adopt network governance, hierarchical organization, and centralised management. However, the effect of management is often not satisfactory. Therefore, this paper takes the outbreak of COVID-19 in 2019 as a case to explore whether complex systems management can provide ideas to disaster response. The study demonstrates the need for complex systems in disaster response by conducting an in-depth analysis of response data in China and Australia, using the case study of the 2019 pandemic outbreak. © 2022 IEEE.

Stacking based prediction of COVID-19 Pandemic by integrating infectious disease dynamics model and traditional machine learning

Accurate prediction of 2019 novel coronavirus diseases (COVID-19) has been playing an important role in making more effective prevention and control policies during pandemic crises. The aim of this paper was to develop an innovative stacking based prediction of COVID-19 pandemic cumulative confirmed cases (StackCPPred) by integrating infectious disease dynamics model and traditional machine learning. Based on population migration characteristics, five feature indicators were first extracted from the population flow data in the early stage of this epidemic, which were collected from the National Health Commission of the People's Republic of China. Then, stacking based ensemble learning (SEL) model was established for COVID-19 prediction using traditional machine learning, including the multiple linear regression (MLR) and the tree regression model (XGBoost and LightGBM). By introducing the variable "death state", an improved Susceptible-Infected-Recovered (ISIR) model was established. Finally, a hybrid model, StackCPPred was proposed by incorporating the ISIR model outputs and the five feature indicators into the SEL model. Real data on population movements and daily cumulative number of newly confirmed cases across the country from January 23 to February 6 were used to validate our model. The results positively proved that the proposed StackCPPred model outperformed the existing models for COVID-19 prediction, as quantified by the root mean square error (RMSE), the root mean square logarithmic error (RMSLE) and the coefficient of determination (R2) (g1/41841 persons, g1/40.1 and >0.9, respectively). Furthermore, this study confirms the validity and usefulness of the StackCPPred model for COVID-19 prediction. © 2022 ACM.

The SARS-CoV-2 rS1-E-PLGA nanovaccine and evaluation of its immune effect in BALB/c mice.

OBJECTIVE: Vaccination is an important method for preventing COVID-19 infection. However, certain vaccines do not meet the current needs. To improve the vaccine effect, discard ineffective antigens, and focus on high-quality antigenic clusters, S1-E bivalent antigens were designed. MATERIALS AND METHODS: Vaccine delivery is performed using poly (lactic-co-glycolic acid) (PLGA). Here, the recombinant S1-E (rS1-E) was covered on PLGA and injected intramuscularly into mice. In total, 48 BALB/c mice were randomly divided into six groups with 8 mice in each group. The mice received intramuscular injections. Prior to vaccination, the hydrophobicity of the rS1-E and the antigenic site of the E protein were both analysed. The morphology, zeta potential, and particle size distribution of rS1-E-PLGA were examined. Anti-S1 and anti-E antibodies were detected in mouse serum by ELISA. Neutralising an-tibodies were detected by co-incubating the pseudovirus with the obtained serum. IL-2 and TNF-α levels were also measured. RESULTS: The designed recombinant S1-E protein was successfully coated on PLGA nanoparticles. rS1-E-PLGA nanovaccine has suitable size, shape, good stability, sustained release and other characteristics. Importantly, mice were stimulated with rS1-E-PLGA nanovaccines to produce high-titre antibodies and a good cellular immune response. CONCLUSIONS: Our results indicate that rS1-E-PLGA nanovaccine may provide a good protective effect, and the vaccine should be further investigated in human clinical trials for use in vaccination or as a booster.

Real-Time Measurement of Alcohol Vapours Released from Alcohol-based Hand Disinfectants and User Habits Study of Hong Kong Residents in the Pandemic of COVID-19

Based on the information suggested by World Health Organization (WHO) and Hong Kong Special Administrative Region (HKSAR) government, wearing a mask and sterilizing hands with alcohol-based hand disinfectants are effective ways to maintain good personal hygiene to prevent viral infections. This study focused on the real-time concentrations of alcohol vapor in the air associated with five alcohol-based hand disinfectants. The results indicated that the alcohol concentrations increased dramatically (max. ~46,000 ppb/g sample) in the hand-rubbing process. Hong Kong residents' survey on habits of using such disinfectants showed that 65% of people use them daily and 34% of people use them ≥ 5 times per day, indicating a high frequency of usage. About 79% of respondents claimed to have skin problems, and 18% got eyes discomfort when using these disinfectants. Despite the potential health risks of using alcohol disinfectants remaining unclear, such a large amount and frequent usage should be aware of potential health problems in the long term. © 2022 by the authors.

CLL-VR: A UK study of vaccine responses in patients with B-cell chronic lymphocytic leukaemia

B-cell chronic lymphocytic leukaemia (CLL) is associated with immune suppression and patients are at increased risk following SARS-CoV-2 infection. The Chronic Lymphocytic Leukaemia-Vaccine Response (CLL-VR) study was designed to assess immune responses following the introduction of Covid-19 vaccination in UK. Five hundred patients with CLL were recruited nationally through NHS and charity communications. Phlebotomy blood samples were taken from local patients ( n = 100) and dried blood spot samples were collected via post from participants across the UK ( n = 400). Ninety-six age-matched control subjects were also recruited locally. Samples were taken at 2-3 weeks following the first, second and third primary vaccine doses. Antibody and cellular responses against spike protein, and neutralising antibody titre to delta and omicron variant, were measured. Total serum immunoglobulin level was also determined. Responses were analysed according to clinical history, serum immunoglobulin level and vaccine type received. Donors with a clinical or serological history of prior natural infection were excluded from the analysis. Twenty percent (70/353) of participants developed a measurable antibody response after the first vaccination and this increased to 67% (323/486) following the second dose and 80% (202/254) after a third dose. The response rate in healthy controls plateaued at 100% after only two doses. The magnitude of the antibody response was also 3.7-fold lower following the second vaccine compared to controls ( n = 244;490 vs. 1821 U/ml, p < 0.0001) but increased markedly to 3114 U/ ml after third dose ( n = 51). No difference was observed in relation to the initial vaccine platform received. Multivariate analysis on 486 participants showed that BTKi therapy, history of recurrent infection and low serum antibody levels of IgA or IgM were independent prognostic markers for poor antibody response. Among participants with a detectable antibody response, a marked reduction in the ability to neutralise the delta and omicron variants of concern was noted compared to healthy controls following both the second and third dose of vaccine. Cellular responses were assessed following the second vaccine by IFN-g ELISPOT ( n = 91). Patients who had received the ChAdOx1 vaccine had similar levels to controls ( p = 0.39), while those who had received BNT162b2 had lower levels ( p < 0.0001). Five patients with poor spike-specific antibody responses to vaccination subsequently developed breakthrough infection with SARS-CoV-2 delta variant. Antibody responses and neutralisation remained poor following recovery from infection although T-cell responses were strong and only one patient required hospital admission. CLL-VR is the largest vaccine study conducted in patients with CLL and reveals diminished but comparable antibody responses to both the ChAdOx1 and BNT162b2 vaccines with some improvement following third primary dose of mRNA vaccine. In contrast T-cell responses following second dose are greater in those who received ChAdOx1 platform. Low neutralising activity against the delta and omicron variants highlights an ongoing risk for this vulnerable population despite repeated vaccination and reveals the need for alternative approaches to protection including prophylactic monoclonal antibody therapy..

Coronavirus Epidemic (COVID-19) Prediction and Trend Analysis Based on Time Series

In the global fight against the novel corona-virus pneumonia epidemic (COVID-19), a reasonable prediction of the spread of the epidemic has important reference significance for epidemic prevention and control. In order to solve the problem of time series prediction and analysis of the epidemic with limited sample data, nonlinear and high-dimensional features, this study applies the Nonlinear Auto-Regressive neural network (NAR) model for machine learning. The paper predicts the development of the epidemic in the two dimensions of the number of confirmed cases and the number of deaths in major countries in the world, and compares NAR with the traditional Logistic Regression (LR), the classic time series model ARIMA and the SEIR infectious disease dynamic model. This research provides rapid decision-making and new ideas for countries to respond to the 'post-epidemic era'. © 2021 IEEE.

Query and Clustering of Spatio-Temporal Trajectory Big Data under the Background of COVID-19

With the continuous development of sensor technology, computer technology, artificial intelligence and other advanced technologies, there are more and more researches on trajectory tracking and detection technology, which have been widely used in urban planning, traffic management, safety control and other aspects. Trajectory tracking and detection has always been the focus of research by experts and scholars. The purpose of this study is to track and detect the spatial trajectory of the infected person under the current new crown virus epidemic, to timely and accurately understand the itinerary of the new crown virus infected person and to find out all the suspected contacts that the infected person may come into contact with. The current epidemic situation in various countries has made a certain contribution. © 2021 ACM.

Antibody responses after first and second Covid-19 vaccination in patients with chronic lymphocytic leukaemia.

B-cell chronic lymphocytic leukaemia (CLL) is associated with immunosuppression and patients are at increased clinical risk following SARS-CoV-2 infection. Covid-19 vaccines offer the potential for protection against severe infection but relatively little is known regarding the profile of the antibody response following first or second vaccination. We studied spike-specific antibody responses following first and/or second Covid-19 vaccination in 299 patients with CLL compared with healthy donors. 286 patients underwent extended interval (10-12 week) vaccination. 154 patients received the BNT162b2 mRNA vaccine and 145 patients received ChAdOx1. Blood samples were taken either by venepuncture or as dried blood spots on filter paper. Spike-specific antibody responses were detectable in 34% of patients with CLL after one vaccine (n = 267) compared to 94% in healthy donors with antibody titres 104-fold lower in the patient group. Antibody responses increased to 75% after second vaccine (n = 55), compared to 100% in healthy donors, although titres remained lower. Multivariate analysis showed that current treatment with BTK inhibitors or IgA deficiency were independently associated with failure to generate an antibody response after the second vaccine. This work supports the need for optimisation of vaccination strategy in patients with CLL including the potential utility of booster vaccines.

A Comprehensive Predictive Evaluation Model Based on T-S Fuzzy Neural Network and Regression Fitting Cross Analysis

With the outbreak of COVID-19 at the end of 2019, under the requirement of in-depth study and implementation of the overall national security concept, people's health level has become the focus of people's attention, and it is also the most basic and fundamental important indicator to reflect people's livelihood. Taking Shenzhen, a city with strong comprehensive economic level, as an example, this paper uses data processing to select six major influencing factors, such as medical treatment and environment, and uses the method of regression and fitting crossover analysis to establish the fitting curve between factors and people's health level for prediction, and obtains the regression equation. On this basis, T-S Fuzzy Neural Network (T-S FNN) is used to divide the evaluation grade of regression model, make an effective evaluation of multiple factors of people's physical health level, establish a comprehensive prediction evaluation model, and obtain the gradient grade of factors affecting people's physical health correlation and their own direct factors. © 2020 IEEE.