Ming Qi

The West African region has experienced high economic development. With the increasing energy consumption and emissions, how to coordinate the relationship among energy consumption, trade opening, and economic growth, and how to develop a low-carbon development pattern are becoming the most important issues in West Africa. This paper uses the tri-variable Toda-Yamamoto model to investigate the dynamic interactions among energy consumption, economic growth, and trade in West Africa. The findings indicate that, first, the positive impact of energy consumption on economic growth in West Africa has shown a significant lag effect, and energy consumption has a strong trade-dependent relationship to economic promotion. Trade opening and economic growth in West Africa are mutually reinforcing in the long run. Next, the role of foreign trade in boosting economic growth is more significant in countries with lower levels of economic development. Finally, when replacing the energy consumption indicator with CO₂ emissions, the results remain robust. Considering the regional development differences, grouping countries by GDP per capita reveals that there exists a bilateral causal relationship between energy consumption and trade openness in the higher economic development group. The impact of trade openness on economic growth is more remarkable in countries with lower levels of economic development in West Africa. The findings have important implications for policymakers in understanding the economic development pattern of West Africa. It is necessary for West African countries to develop an integrated energy and trade policy in order to maintain long-term sustainable economic growth.

Purpose: In this paper, the authors use the balance sheet data to investigate the interconnectedness and risk contagion effects in China's banking sector. They firstly study the network structure and centrality of the interbank network. Then, they investigate how and to what extent the credit shock and liquidity shock can lead to the risk propagation in the banking network.Design/methodology/approach: Referring to the theoretical framework by Haldane and May (2011), this paper uses the network topology theory to analyze the contagion mechanism of credit shock and liquidity shock. Centrality measures and log-log plot are used to evaluate the interconnectedness of China's banking network.Findings: The network topology has shown clustering effects of large banks in China's financial network. If the Industrial and Commercial Bank of China (ICBC) is in distress, the credit shock has little impact on the Chinese banking sector. However, the liquidity shock has shown more substantial effects than that of the credit shock. The discount rate and the rollover ratio play significant roles in determining the contagion effects. If the credit shock and liquidity shock coincide, the contagion effects will be amplified.Research limitations/implications: The results of this paper reveal the network structure of China's interbank market and the resilience of banking system to the adverse shock. The findings are valuable for regulators to make policies and supervise the systemic important banks.Originality/value: The balance sheet data of different types of banks are used to construct a bilateral exposure matrix. Based on the matrix, this paper investigates the knock-on effects of credit shock triggered by the debt default in the interbank market, the knock-on effects of liquidity effects, which is featured by “fire sale” of bank assets, and the contagion effects of combined shocks.

Economic development in the “new era” will require green innovation. To encourage the growth of green technology innovation, it has become fashionable to strengthen environmental regulation. However, the impact of environmental regulation on green technology innovation, as well as the role of government subsidies, needs to be examined. Utilizing fixed-effect models and 2SLS models to explore the impact of environmental regulation on green technology innovation in China from 2003 to 2017, this research sought to examine whether environmental regulations impact green technology innovation, as well as the role of government subsidies in the above-mentioned influence path. The findings support the Porter Hypothesis by demonstrating an inverted “U” relationship between environmental regulation and green technology innovation. The impact of environmental regulation on green technology innovation varies by region. To be specific, there is an inverted “U” relationship between environmental regulation and green technology innovation in China’s central and central coast regions. In comparison, the north area, southern coast, and southwest region exhibit a “U” relationship between the two. The relationship is not significant in the Beijing-Tianjin region. Additionally, government subsidies act as an intermediate in this process, positively influencing firms to pursue green technology innovation during the earliest stages of environmental regulation strengthening. However, government subsidies above a certain level are unproductive and should be used appropriately and phased off in due course.

Purpose: In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.Design/methodology/approach: By the means of RAS algorithm, the interconnection among financial institutions are illustrated. Different methods, including Linear Granger, Systemic impact index (SII), vulnerability index (VI), CoVaR, and MES are used to measure the systemic risk exposures across different institutions.Findings: The results illustrate that big banks are more interconnected and hold the biggest scales of inter-bank transactions in the financial network. The institutions which have larger size tend to have more connection with others. Insurance and security companies contribute more to the systemic risk where as other institutions, such as trusts, financial companies, etc. may bring about severe loss and endanger the financial system as a whole.Practical implications: Since other institutions with low levels of regulation may bring about higher extreme loss and suffer the whole system, it deserves more attention by regulators considering the contagion of potential risks in the financial system.Originality/value: This study builds a valuable contribution by examine the systemic risks from the perspectives of both interconnection and tail risk measures. Furthermore; Four types financial institutions are investigated in this paper.

In this paper, a risk optimization model is proposed to minimize the oil supply risk from the perspective of diversification. The results show that there is large room for both China and India to improve the oil supply security. China should reduce oil imports from Saudi Arabia and Russia while increasing oil imports from the United States and Kazakhstan. India should import more oil from America and Russia while substantially reducing imports from Iraq and Saudi Arabia. In terms of the regional analysis, the Middle East plays a crucial role in the oil import strategies. They account for almost half of the total oil imports to China and India. African countries provide an alternative choice to diversify their energy supply risks. Based on forecasts of oil demand, we investigate the optimal oil import strategies for both countries until 2030 and 2040. China’s imports from the United States and Kazakhstan are forecasted to increase by more than ten times by 2030. India should import four times as much oil from the United States as it does now and import 10 times more crude oil from Russia. Africa and North America will play a more important role in India’s oil supply security.

Advances in machine learning have led to an emergence of new paradigms in the analysis of large data which could assist traditional approaches in the search for new physics amongst the immense Standard Model backgrounds at the Large Hadron Collider. Deep learning is one such paradigm. In this work, we first study feature importance ranking of signal-background classification features with deep learning for two Beyond Standard Model benchmark cases: a multi-Higgs and a supersymmetry scenario. We find that the discovery reach for the multi-Higgs scenario could still increase with additional features. In addition, we also present a deep learning-based approach to construct new features to separate signals from backgrounds using the ATLAS detector as a specific example. We show that the constructed feature is more effective in signal-background separation than commonly used features, and thus is better for physics searches in the detector. As a side application, the constructed feature may be used to identify any momentum bias in a detector. We also utilize a convolutional neural network as part of the momentum bias checking approach.