Is China’s green growth possible? The roles of green trade and green energy

In tandem with global initiatives to ‘go green’, China is undertaking a series of steps to achieve green economic growth. To investigate the dynamic nexus of green growth, green trade, and green energy (3 G) in China, an index is developed in this study to assess the level of provincial green growth by employing five types of indicators – economic growth, environmental pollution loss, carbon emissions loss, natural resource loss, and environmental and natural resource benefits. Then, this paper uses the SYS-GMM method to explore the influences of green trade and green energy on green growth by using data compiled from 30 provinces in China over the period 2007–2016. Furthermore, we check the potential heterogeneity, asymmetry, and internal mediating mechanism of the 3 G nexus. The main findings are highlighted as follows: (1) Green trade and green energy can accelerate China’s green growth; (2) enhancing medium- and high-technology green trade can contribute to improving local green growth; (3) this impact is heterogeneous in regions with different trade levels, and asymmetric at various quantiles for the full panel; (4) the positive investment effect, labour effect, and technical effect are effective mediators of the nexus between green trade and green growth

How green growth affects carbon emissions in China: the role of green finance

Accelerating the green transition of the economy is an effective way to conserve energy and reduce emissions, and its impact on the greenhouse effect deserves in-depth discussion. Based on this, we examine the potential effect of China’s green growth on carbon dioxide (CO2) emissions by applying provincial panel data from 2004 to 2018. The regional heterogeneity and how does green finance affect the green growth-CO2 nexus are also checked. The primary findings imply that: (i) China’s green growth achieves preliminary results, and its impact on CO2 emissions is significantly negative. Also, green finance can facilitate carbon emission reduction; (ii) significant regional heterogeneity exists within various regions. Only in the central and western regions can green growth effectively reduce CO2 emissions, and in the eastern and central regions, green finance is conducive to promoting carbon reduction; and (iii) the mediating role of green finance is significant. In other words, China’s green growth not only mitigates the greenhouse effect directly, but also affects CO2 emissions indirectly by accelerating the development of green financ

Secure Communication Based on a Hyperchaotic System with Disturbances

This paper studies the problem on chaotic secure communication, and a new hyperchaotic system is included for the scheme design. Based on Lyapunov method and H∞ techniques, two kinds of chaotic secure communication schemes in the case that system disturbances exist are presented for the possible application in real engineering; corresponding theoretical derivations are also provided. In the end, some typical numerical simulations are carried out to demonstrate the effectiveness of the proposed schemes

Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach

Highway traffic crashes exert a considerable impact on both transportation systems and the economy. In this context, accurate and dependable emergency responses are crucial for effective traffic management. However, the influence of crashes on traffic status varies across diverse factors and may be biased due to selection bias. Therefore, there arises a necessity to accurately estimate the heterogeneous causal effects of crashes, thereby providing essential insights to facilitate individual-level emergency decision-making. This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed. The Neyman-Rubin Causal Model (RCM) is employed to formulate this problem from a causal perspective. The Conditional Shapley Value Index (CSVI) is proposed based on causal graph theory to filter adverse variables, and the Structural Causal Model (SCM) is then adopted to define the statistical estimand for causal effects. The treatment effects are estimated by Doubly Robust Learning (DRL) methods, which combine doubly robust causal inference with classification and regression machine learning models. Experimental results from 4815 crashes on Highway Interstate 5 in Washington State reveal the heterogeneous treatment effects of crashes at varying distances and durations. The rear-end crashes cause more severe congestion and longer durations than other types of crashes, and the sideswipe crashes have the longest delayed impact. Additionally, the findings show that rear-end crashes affect traffic greater at night, while crash to objects has the most significant influence during peak hours. Statistical hypothesis tests, error metrics based on matched "counterfactual outcomes", and sensitive analyses are employed for assessment, and the results validate the accuracy and effectiveness of our method.Comment: 38 pages, 13 figures, 8 table

Can expanding natural gas infrastructure mitigate CO2 emissions? Analysis of heterogeneous and mediation effects for China

To verify whether the expansion of natural gas infrastructure can effectively mitigate carbon dioxide (CO2) emissions in China, this study first investigates the impact of natural gas infrastructure on China's CO2 emissions by employing a balanced panel dataset for 30 Chinese provinces covering 2004–2017. Fully considering the potential heterogeneity and asymmetry, the two-step panel quantile regression approach is utilized. Also, to test the mediation impact mechanism between natural gas infrastructure and CO2 emissions, this study then analyzes the three major mediation effects of natural gas infrastructure on China's CO2 emissions (i.e., scale effect, technique effect, and structure effect). The empirical results indicate that expansion of the natural gas infrastructure can effectively mitigate China's CO2 emissions; however, this impact is significantly heterogeneous and asymmetric across quantiles. Furthermore, through analyzing the mediation impact mechanism, the natural gas infrastructure can indirectly affect CO2 emissions in China through the scale effect (i.e., gas population and economic effects) and structure effect (i.e., energy structure effect). Conversely, the technique effect (i.e., energy intensity effect) brought by natural gas infrastructure on CO2 emissions in China has not been significant so far. Finally, policy implications are highlighted for the Chinese government with respect to reducing CO2 emissions and promoting growth in the natural gas infrastructure

How natural disasters affect carbon emissions: the global case.

The outbreak of the COVID-19 pandemic has once again made the impacts of natural disasters a hot topic in academia. The environmental impacts of natural disasters, however, remain unsettled in the existing literature. This study aims to investigate the impact of natural disasters on CO2 emissions. For this purpose, we employ a panel dataset covering 138 countries over the period 1990-2018 and two dynamic panel estimation methods. Then, considering the differences in CO2 emissions across various countries, we run a panel quantile regression to examine the asymmetry in the nexus between natural disasters and CO2 emissions. We also discuss the mediating effects of energy consumption between natural disasters and CO2 emissions. After conducting a series of robustness checks, we confirm that our results are stable and convincing. The empirical results indicate that natural disasters significantly reduce CO2 emissions. Nevertheless, the impact of natural disasters on CO2 emissions is asymmetric across different quantiles of CO2 emissions. Furthermore, the technology level serves as an important moderating factor between natural disasters and CO2 emissions. The mediating effect results reveal that natural disasters not only directly reduce CO2 emissions but also indirectly promote carbon reduction by restraining energy consumption. Finally, several policy implications are provided to reduce CO2 emissions and the damage caused by natural disasters

Analytical Approach to Quantitative Country Risk Assessment for the Belt and Road Initiative

In recent years, the “Belt and Road Initiative” (BRI) promoted by the Chinese government has attracted a significant amount of international trade and transnational investment and other businesses. Accordingly, country risk assessment should be granted priority in the decision-making process for these projects. Based on a comprehensive consideration of important relevant countries and the availability of data of countries along the BRI, this paper uses data from 49 countries along the BRI between 2014–2019 and establishes a national risk-evaluation system for the BRI from four dimensions (i.e., political, economic, social, and investment). This paper adopts the Grey correlation analysis based on the Technique for Order Preference by Similarity to Ideal Solution (Grey-TOPSIS) method to identify and evaluate the risk of countries along the BRI. Geographic Information System (GIS) maps are drawn according to the criteria for classifying the five risk levels to show the rank of the four aspects of risk scores along the BRI in 2019 and the rank of overall country risk scores during the period 2014–2019. The proposed conclusion and policy implications can help the Chinese government and companies to make informed decisions and minimize potential risks