Greenhouse gases, especially carbon dioxide (CO2) emissions, are viewed as one of the core causes of climate change, and it has become one of the most important environmental problems in the world. This paper attempts to investigate the relation between CO2 emissions and economic growth, industry structure, urbanization, research and development (R&D) investment, actual use of foreign capital, and growth rate of energy consumption in China between 2000 and 2018. This study is important for China as it has pledged to peak its carbon dioxide emissions (CO2) by 2030 and achieve carbon neutrality by 2060. We apply a suite of machine learning algorithms on the training set of data, 2000–2015, and predict the levels of CO2 emissions for the testing set, 2016–2018. Employing rmse for model selection, results show that the nonlinear model of k-nearest neighbors (KNN) model performs the best among linear models, nonlinear models, ensemble models, and artificial neural networks for the present dataset. Using KNN model, sensitivity analysis of CO2 emissions around its centroid position was conducted. The findings indicate that not all provinces should develop its industrialization. Some provinces should stay at relatively mild industrialization stage while selected others should develop theirs as quickly as possible. It is because CO2 emissions will eventually decrease after saturation point. In terms of urbanization, there is an optimal range for a province. At the optimal range, the CO2 emissions would be at a minimum, and it is likely a result of technological innovation in energy usage and efficiency. Moreover, China should increase its R&D investment intensity from the present level as it will decrease CO2 emissions. If R&D reinvestment is associated with actual use of foreign capital, policy makers should prioritize the use of foreign capital for R&D investment on green technology. Last, economic growth requires consuming energy. However, policy makers must refrain from consuming energy beyond a certain optimal growth rate. The above findings provide a guide to policy makers to achieve dual-carbon strategy while sustaining economic development.
The outbreak of coronavirus pandemic in late 2019 posted unprecedented social-economic challenges and disruptions to societies and individuals. The “new-normal” styles of living and working could intertwined with other determinants complicating the investigation of individual’s financial vulnerability. The purpose of this paper is to conduct literature survey to review and consolidate the recent scattered literatures to identify some possible factors to be considered in the research related to financial vulnerability, including pandemic’s impact of COVID-19 to different aspects of personal finance issues, pandemic-driven digitisation of the economy activities, changes in financial behaviour and addiction to digital technology.
This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.
This paper proposes a method that uses volatility index of US and six other markets of Pacific Basin, namely Hong Kong, Australia, India, Japan, Korea, and China, to provide value-at-risk (VaR) and expected shortfall (ES) forecasts. Empirical constants that are used to multiply the levels of volatility indexes for estimating VaR and ES of various significance levels for 1–22 days ahead, one by one, for seven market indexes have been statistically determined using daily data spanning from 4.75 to 16 years. It is because it would be inappropriate to simply scale up the one-day volatility by multiplying the square root of time (or the number of days) ahead to determine the risk over a longer horizon of [Formula: see text] days. Results show that the shift to ES approach generally increases the regulatory capital requirements from 2.09% of India market to 8.56% of Korea market except for the China market where ES approach yields an unexpected decrease of 3.21% of capital requirement.
Businesses have been exposed to various challenges during the global pandemic. Unfortunately, the financially vulnerable groups in society are disproportionately affected by such a difficult time. Therefore, it is important for businesses to recognise this when creating new business models for sustainable corporate management. This paper attempts to (1) identify the factors that affect individual financial vulnerability, (2) develop survey items to assess financial vulnerability and its factors and (3) provide the characteristics of financially vulnerable groups by presenting a complete set of descriptive statistics. The results can help to create more inclusive business models that are better equipped to address the challenges ahead. A questionnaire-based survey was conducted with collaboration with an NGO that provides a financial counselling service in Hong Kong. In total, 338 valid responses were collected and the data were used to characterise financially vulnerable groups in terms of (1) change in financial conditions due to COVID-19; (2) exposure to digitised financial services and related push marketing; (3) financial management ability; (4) changes in four financial behaviours and (5) financial vulnerability as measured according to the debt/service ratio. Results show that the respondents have a median debt/service ratio of 0.513, which represents an unsustainable level of debt. Around ¼ of surveyed respondents reported that their debt/service ratio was 1 or even higher, indicating obvious difficulties in meeting financial obligations. A total of 36.7% of the respondents reported worsening financial conditions since the outbreak of COVID-19. The results presented provide a solid empirical set of data that will help future research work to examine and/or develop a heuristic financial vulnerability model that incorporates the key factors leading to it. Businesses can refer to them when creating new business models that are sustainable, able to meet corporate social responsibility goals and can achieve several targets/goals of the United Nations’ Sustainable Development Goals.
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