Guoqing Zhai

59298590600

Publications - 4

Harnessing artificial intelligence for urban economic resilience

Hyoungsuk Lee Xin Zhao Xiaowei Ma Guoqing Zhai Nicholas Apergis

Publication Name: Applied Economics

Publication Date: 2026-01-01

Volume: 58

Issue: 25

Page Range: 4955-4974

Description:

Amid escalating global economic uncertainty, a comprehensive analysis of the effect of artificial intelligence (AI) development on urban economic resilience (UER) is crucial for promoting sustainable global economic development. This study utilizes panel data from 284 Chinese cities from 2010 to 2022 to empirically test the influence of urban AI on UER and its role mechanism by using the fixed-effects, mediating-effects, and moderating-effects models. The study reveals that AI significantly enhances UER, with an improvement of 7.44%. Harnessing AI for UER remains valid even after conducting the robustness and endogeneity tests. Mechanism analysis discovered that AI enhances UER by increasing urban innovation ability. Industrial structure and wage structure positively moderate the effect of AI on UER. Heterogeneity analysis demonstrates that the improvement effect of AI level on UER is more evident in large (7.49% increase), southern (5.11% increase), non-resource-based (10.84% increase), and high-economic cities (11.17% increase). This paper discusses the path selection from an AI perspective to enhance UER, which provides a useful reference for cities seeking to navigate the new wave of technological revolution.

Open Access: Yes

DOI: 10.1080/00036846.2025.2501352

Role of energy natural resource productivity and environmental taxation in controlling environmental pollution: Policy-based analysis for regions

Yuriy Bilan Kamel Si Mohammed Abdelmohsen A. Nassani Xin Zhao Xiaowei Ma Guoqing Zhai

Publication Name: Geological Journal

Publication Date: 2024-11-01

Volume: 59

Issue: 11

Page Range: 3068-3079

Description:

The present study explores the impact of energy natural resource productivity and environmental tax on environmental sustainability in six major CO2-emitting economies: the Euro Area, China, South Korea, Japan, the United Kingdom and the United States, from 1997 to 2019. This analysis aims to reveal novel findings and implications for different energy natural resource productivity types and environmental regulations. We employed data regarding leading national and regional CO2 emitters from 1997 to 2020 to conduct an empirical analysis using the panel non-linear auto-regressive distributed lag (NARDL) and panel quantile ARDL (QARDL) methods. The results show that energy natural resource productivity and environmental tax are crucial components in reducing CO2 emissions by controlling for innovation technology and renewable energy consumption. The main findings demonstrate that the impact is stronger in the presence of increased energy natural resource productivity and vice versa. These findings have novel implications for sustainable development and carbon neutrality.

Open Access: Yes

DOI: 10.1002/gj.5047

New development: E-government, open data and citizen participation for G20 sustainable development

Xin Zhao Xiaowei Ma Pardaev Jamshid Guoqing Zhai Mohamed Djafar Henni Pardaev Jamshid

Publication Name: Public Money and Management

Publication Date: 2026-01-01

Volume: 46

Issue: 5

Page Range: 648-652

Description:

IMPACT: This article examines how the development of e-government (EGDI), open data, and citizen participation (EPI) jointly influence the G20 countries in achieving the Sustainable Development Goals (SDGs). EGDI is shown to have a positive influence on progress towards delivering the SDGs. Data openness and EPI further strengthen this effect. The strongest impact emerges under conditions of simultaneous implementation. The findings offer actionable insights for public sector accountants, auditors, policy-makers, and digital government strategists concerned with digital reform and sustainable development.

Open Access: Yes

DOI: 10.1080/09540962.2026.2639622

Assessing climate uncertainty in green bonds: Evidence from machine learning and GARCH-MIDAS models

Yang Xie Kaouther Chebbi Hyoungsuk Lee Xin Zhao Xixi Ye Guoqing Zhai

Publication Name: Environmental Impact Assessment Review

Publication Date: 2026-09-01

Volume: 121

Issue: Unknown

Page Range: Unknown

Description:

This paper employs a GARCH-MIDAS framework integrated with machine learning to investigate the impact of climate-related uncertainties on the volatility of the China's green bond market (GBM). By combining high-frequency financial data with multi-source, low-frequency climate uncertainty indicators, we examined how macro-financial conditions and climate risks jointly affect the dynamic changes of the GBM in China. Machine learning methods were used to identify and rank the key drivers of volatility. The research results indicate that traditional macro-financial variables remain the main determinants of the volatility in the green bond market, among which the impact of government bond yields is the most significant. Climate uncertainty information also has a significant impact on the volatility of green bonds. Moreover, incorporating climate uncertainty into the GARCH-MIDAS model significantly enhances its explanatory power, highlighting the importance of considering mixed-frequency risk factors in understanding China's green bond market dynamics. These findings underscore the crucial role of climate uncertainty in green bond pricing and indicate that combining machine learning with mixed-frequency volatility modeling can provide a more comprehensive framework for understanding the dynamics of the green bond market.

Open Access: Yes

DOI: 10.1016/j.eiar.2026.108528