Environmental benefits of blue ecosystem services and residents' willingness to pay in Khulna city, Bangladesh

A deep learning-based approach for predicting COVID-19 diagnosis

This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction. This comparative analysis evaluates the predictive capacities of ARIMA, mathematical modeling, and DQN techniques, aiming to pinpoint the most reliable method for forecasting positive COVID-19 cases. The modeling encompasses COVID-19 cases in Saudi Arabia, the United Kingdom (UK), and Tunisia (TU) spanning from 2020 to 2021. Predicting the number of individuals likely to test positive for COVID-19 poses a challenge, requiring adherence to fundamental assumptions in mathematical and ARIMA projections. The proposed methodology was implemented on a local server. The DQN algorithm formulates a reward function to uphold target functional performance while balancing training and testing periods. The findings indicate that DQN technology surpasses conventional approaches in efficiency and accuracy for predictions.

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.

Blue carbon governance for carbon neutrality in China: Policy evaluation and perspectives

As a nature-based solution for climate change mitigation, blue carbon has been highlighted for realizing carbon neutrality in China. China's central and local governments have issued many policies to promote blue carbon protection and development. However, scaling up blue carbon restoration and protection is required for its substantial contribution to carbon neutrality. This study evaluates the characteristics of China's blue carbon policies using qualitative document analysis based on policy instrument theory. The distribution of different policy instruments among blue carbon policies and resources is analyzed. Suggestions for improving blue carbon policy supply are put forward combined with comparative experience from international organizations and other jurisdictions. The following policies should be strengthened to secure efficient blue carbon protection, restoration, and creation: blue carbon protection legislation, marine ecological compensation system, stable investment in blue carbon projects, integrated blue carbon verification system, and inclusion of blue carbon in regulated carbon markets.

Interactive influences of ecosystem services and socioeconomic factors on watershed eco-compensation standard "popularization" based on natural based solutions

Watershed eco-compensation is a policy tool to realize watershed environment improvement and regional economic development. It is important to eliminate the influence of economic differences between upstream & downstream regions and realize the fairness of regional social development based on Natural based Solutions (NbS). At present, lack of clarity in coupling and coordination analysis of ecosystem services & socioeconomic based on NbS could hamper watershed eco-compensation standards "popularization" and reduce the ability to successfully ecological governance. To meet the needs of economic development and ecological service value realization, dynamic equilibrium game research based on multidimensional relationship coordination and a multi-objective optimization solution of economic benefit distribution was carried out. To achieve the bargaining Bayesian/Nash equilibrium of the watershed eco-compensation standard in the game, the existence conditions of the equilibrium solution of the eco-compensation standard based on the mixed equilibrium game implementation process were studied. To carry out the complete information dynamic game, the equilibrium solution of the watershed eco-compensation standard based on the dynamic transfer payment was solved, and the rational analysis of the dynamic Bayesian equilibrium game of bargaining based on the incentive compatibility mechanism was also discussed. Water quantity and quality eco-compensation can ensure balanced development between ecological protection and the social economy in the Mihe River Basin. Combined with the variation law of socioeconomic water intake-utilization standards and the water use value, the city of Shouguang City & Qingzhou City should pay Linqu County 4.78 million US$ and 1.29 million US$ as watershed eco-compensation standards per year based on NbS, respectively. To verify the rationality of the results derived from the economically optimal model, two modes of "bargaining" & "perfect competition", were used to study the characteristics of the protocols generated by the equilibrium game, and the applicable conditions of the nonzero-sum game solution upstream and downstream of the watershed were also explored. Based on the nonzero-sum processing of the survey results, the current relationship between the input value of eco-compensation and the willingness to pay satisfies v ≥ c + 1 / 4 . Based on the dynamic game & Bayesian equilibrium solution of bargaining, the watershed eco-compensation quota of water quantity & quality is 6.07 million US$, the willingness to pay is 65.63 US$/month. These findings contribute to the quantifying process of bargaining & dynamic equilibrium by transforming "ambiguous" information to achieve sustainable ecosystem service management and develop socioeconomic strategies associated with different compensation features based on NbS, thus helping to inform watershed management.

Assessing the environmental and social co-benefits and disbenefits of natural risk management measures

Risk management measures (RMM) participate in the sustainability of cities and communities through the protection of these socio-eco-environmental systems against threatening events, and by ensuring system recovery. They include structural measures that are grey or green/blue solutions, or hybrid solutions combining the two former types. These measures can provide environmental and social co-benefits (e.g., improved biodiversity, recreational services) and disbenefits (e.g., the development of unwanted flora, concentrations of pollutants). The aim of this article is to provide an approach to assess and compare RMMs by considering these different dimensions. An application to three natural hazards - floods, coastal floods and wildfires - is proposed. The approach takes the form of a procedure to assess the co-benefits/disbenefits of the various RMMs and some technical specifications. It allows comparing the performances of one RMM against another and collectively discussing the choice of RMMs that takes into account a wide range of dimensions. The approach is based on the formulation of eight sustainability criteria and thirty-one indicators. The results were graphically displayed as several types of diagram: one radar chart per RMM, compiling all the indicators; one radar chart by type of risk studied (flood, wildfire and coastal flooding) based on averages of indicators per criterion; a table of the global score assigned to each RMM calculated with an arithmetic mean or a weighted mean. The approach relies on an interdisciplinary research team and involves end-users in a focus group for the validation step. This approach constitutes a transparent base for decision-making processes in the context of sustainable spatial planning against natural risks.