Against the backdrop of a complex global economic landscape, foreign trade serves as a critical link integrating China’s marine economy with the global market, playing an indispensable role in advancing high-quality marine economic development in China and realizing the strategic goal of building a strong maritime nation. Utilizing panel data covering 11 coastal provinces and municipalities in China from 2013 to 2022, this research adopts a double machine learning approach to examine the effects and mechanisms through which the high-quality development of foreign trade (HQD) shapes high-quality marine economic development (THQ) in China. The empirical results demonstrate that (1) high-quality development of foreign trade significantly promotes high-quality marine economic development in China, with a 1-unit increase in the former corresponding to a 1.437-unit rise in the latter. This finding withstands multiple robustness checks. (2) Mechanism analysis indicates that this promotion occurs through three channels: strengthening marine environmental regulation, enhancing marine labor productivity, and upgrading the marine industrial structure. (3) Heterogeneity analysis shows that the effect of high-quality foreign trade is stronger in China’s eastern marine economic region. Simultaneously, the trade development environment emerges as a key factor exerting a significantly positive influence on marine economic quality during China’s foreign trade advancement. The empirical findings propose the following optimization countermeasures for high-quality marine economic development: strengthening marine environmental regulation, enhancing marine labor productivity, and promoting the upgrading of the marine industrial structure.

From the perspective of building a maritime community with a shared future, against the backdrop of the deepening integration of the China–ASEAN Free Trade Area and the continuous release of policy dividends of the Regional Comprehensive Economic Partnership, fisheries trade has become a vital component of regional economic cooperation and an important link in the sustainable development of global fisheries. Nevertheless, academic controversies still remain regarding the differentiated driving roles and relative importance of economic, institutional, and geographical factors in China’s fisheries exports to ASEAN countries. Based on the panel data of China’s fisheries exports to 10 ASEAN countries from 2013 to 2025, this paper constructs a time-varying stochastic frontier gravity model to empirically investigate the influencing effects and relative contribution of the three dimensional factors. The empirical results indicate that economic factors act as the primary driving force, with per capita GDP and foreign exchange reserves exerting prominent positive impacts whose influence intensity is far greater than that of geographical factors. Institutional factors present a significant negative constraining effect, revealing that bilateral institutional compatibility serves as a critical restrictive condition for fisheries trade. Geographical factors also impose an obvious inhibitory impact on fisheries trade, yet their influence is weaker than economic and institutional factors and can be partially offset by optimizing the institutional environment. In addition, fisheries trade efficiency exhibits distinct time-varying characteristics and obvious country heterogeneity, and there remains considerable untapped export potential for China’s fisheries trade with Singapore, Indonesia, Thailand and Malaysia. This study clarifies the hierarchical structure and primary-secondary relationship among economic, institutional and geographical driving factors, and provides empirical evidence and decision-making references for promoting high-quality and sustainable fisheries cooperation between China and ASEAN under the framework of the maritime community with a shared future.

Against the backdrop of accelerating global urbanization and intensifying ecological pressures, investigating the relationship between urbanization levels and ecological resilience in resource-based cities has become crucial for nations striving to achieve both sustainable development and ecological conservation. Utilizing panel data from 114 resource-based cities in China between 2010 and 2023, this study innovatively employs a composite nighttime light index to measure urbanization levels and constructs a comprehensive ecological resilience index using the entropy method. By applying a double machine learning model, this study thoroughly examines the impact, mechanisms, and heterogeneity of urbanization on ecological resilience in these cities. The findings reveal a gradual increase in ecological resilience among China’s resource-based cities, with the majority reaching high resilience levels by 2023. Spatial aggregation centers are identified in eastern China, the Yangtze River Delta, and the Pearl River Delta. Moreover, urbanization demonstrates a significant positive correlation with ecological resilience, a conclusion reinforced through robustness tests. Mechanism analysis reveals that industrial structure upgrading, green technology innovation, and energy efficiency improvement serve as key transmission channels. Heterogeneity analysis indicates that urbanization exerts a more pronounced effect on enhancing ecological resilience in regenerative resource-based cities as well as those located in eastern and central regions, while its impact is relatively weaker in declining resource-based cities and those in western and northeastern regions. Finally, this study proposes policy recommendations focusing on advancing industrial structure sophistication, constructing a green technology innovation ecosystem, implementing an energy efficiency enhancement initiative, deepening region-specific governance, and adopting targeted policy interventions. These findings provide theoretical support for precise policy formulation in resource-based cities and contribute to advancing academic understanding of the relationship between sustainable development and ecological resilience in such regions.

Against the backdrop of the deepening digital economy and accelerated green and low-carbon transformation, data factors have emerged as a core strategic resource driving the formation and allocation of new quality productive forces. Whether data can transition from closure to openness, thereby converting data dividends into inclusive green growth momentum that balances efficiency and equity, constitutes a strategic imperative in the process of Chinese-style modernization. Based on city-level data in China from 2010 to 2024, this paper measures the inclusive green growth (IGG) levels of 295 prefecture-level and above cities, and employs the double/debiased machine learning (DDML) model to examine the impact and underlying mechanisms of public data open platforms on urban IGG, treating the launch of these platforms as a quasi-natural experiment. The findings reveal that the launch of public data open platforms exerts a significant positive effect on urban IGG and is associated with improvements across multiple dimensions, including economic development, social equity, green production and consumption, and environmental protection. In terms of mechanisms, public data open platforms promote IGG through three pathways: advancing digital inclusive finance development, mitigating factor market distortions, and improving urban information circulation. The impact effects exhibit significant heterogeneity across cities with different endowments: cities with larger economic scales, more abundant human capital, and higher initial IGG levels benefit more from public data openness. Furthermore, the effect of public data open platforms on urban IGG displays a regional gradient pattern of “strongest in the east, moderate in the west, and weakest in the central region,” indicating that policy dividends are fully realized in information-intensive economies but remain insufficiently realized in regions with relatively lagging digital infrastructure.

As industrial green transformation advances and urban risks become more complex, this study explores the relationship between China’s national green industrial park policy and urban resilience. The empirical analysis draws on panel data for 293 Chinese cities at the prefecture level or above between 2011 and 2024 and uses a Double/Debiased Machine Learning (DML) framework to flexibly account for observed city characteristics and possible nonlinear relationships. A positive association is observed between the green industrial park policy and the composite urban resilience measure, with this pattern remaining broadly consistent under a range of robustness and sensitivity analyses. Positive coefficients are also obtained across all four resilience components; however, the empirical support is comparatively stronger for the ecological and infrastructure components than for their economic and social counterparts. The pathway results are compatible with potential roles for environmental governance investment, residents’ income, and new-quality productive forces. Differences in the estimated relationships are more pronounced when cities are grouped by resource dependence and industrial-base characteristics, whereas the east–central–west classification does not yield statistically significant between-group variation. By shifting attention from park-specific environmental performance to broader resilience outcomes at the city level, this study extends the scope of green industrial park policy assessment. Given the assumptions underlying the empirical design, the reported coefficients represent conditional relationships rather than conclusive causal evidence.