The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the most active innovation areas in China, but the complex institutional and cultural environment makes innovation cooperation among the cities challenging. Based on 2001–2019 data, this study analyses the spatial pattern and externalities of GBA’s knowledge innovation network using a social network method and spatial econometric model. Results show that Guangzhou and Hong Kong have always been the cores of knowledge innovation in network. Shenzhen emerged as an innovation centre after 2012, and other cities have become peripheral areas in the network. Small- and medium-sized cities do not benefit from the innovative development of core cities but are trapped in their agglomeration shadow. Institutional and cultural differences are the main obstacles hindering innovation cooperation between cities. In comparison, distance has fewer limitations on innovation cooperation. The negative externality of knowledge innovation network indicates that this region should narrow the gap of spatial differences, optimise the innovation network pattern to improve the network externalities.

Economic resilience is a critical indicator of the sustainable development of an urban economy. This paper measures the urban economic resilience (UER) of 286 major cities in China from six indicators—economic growth, opening up, social development, environmental protection, natural conditions, and technological innovation—using a subjective and objective weighting method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. Furthermore, kernel density estimation (KDE) was used to reveal the spatial and temporal trends in UER across cities, and a social opportunity function was applied to access the opportunity for economic resilience and the fairness of opportunities for economic resilience in 19 urban agglomerations in China. The results show that the UER was, in general, low across all cities but increased over time. Geographically, the UER disperses from the eastern coast to inland cities. Amongst urban agglomerations in China, the economic resilience opportunity index also varies spatially and increases over time. On the other hand, the opportunity fairness index of UER remained largely stable and substantial inequalities exist across all urban agglomerations, indicating the need for differentiated policy intervention to ensure equality and the sustainable development of the region. The methodology developed in this research can also be applied in other cities and regions to test its re-applicability and to understand the UER in different contexts.

We analyze the mechanism for industrial co-agglomeration in Chinese 283 cities to affect haze pollution from 2003 to 2016 and examine the possible mediating effects of urbanization and energy structure between haze pollution and industrial co-agglomeration, finally obtaining the following results. First, industrial co-agglomeration and haze pollution across China, including central and eastern regions keep a typical inverted U-shaped curve relationship. That is, industrial co-agglomeration first promotes haze pollution and then restrains it. However, the impact of industrial co-agglomeration on haze pollution in western China is still on the left side of the inverted U-shaped curve, reflecting a promotion effect. Second, industrial co-agglomeration has a significant spatial spillover effect on haze pollution. Additionally, industrial co-agglomeration can promote haze pollution in local regions but inhibit it in surrounding regions in both the short and long run. In contrast, when the industrial co-agglomeration index exceeds the inflection point (3.6531), it benefits the reduction of haze pollution in local regions, while not being conducive to it in the neighboring regions. Third, industrial co-agglomeration can affect haze pollution through urbanization and energy structure, that is, urbanization and energy structure play an intermediary role between them.

Innovation agglomeration plays a decisive role in improving the input–output scale and marginal output efficiency of factors. This paper takes carbon emissions as the unexpected output and energy consumption as the input factor into the traditional output density model. The dynamic spatial panel Durbin model is used to analyze the mechanism for innovation agglomeration and energy intensity to affect carbon emissions from 2004 to 2017 in thirty Chinese provinces. Then, we test the possible mediating effect of energy intensity between innovation agglomeration and carbon emissions. The major findings are as follows. (1) The carbon emission intensity has time-dependence and positive spatial spillover effect. That is, there is a close correlation between current and early carbon emissions, and there is also a high-degree correlation between regional and surrounding areas’ carbon emissions. (2) Carbon emissions keep a classical inverted U-shaped relation with innovation agglomeration, as well as with energy intensity. However, the impact of innovation agglomeration on carbon emissions in inland regions of China does not appear on the right side of the inverted U-shaped curve, while carbon emissions are subject to a positive nonlinear promoting effect from energy intensity. (3) When the logarithm of innovation agglomeration is more than 3.0309, it first shows the inhibition effect on energy intensity. With the logarithm of innovation agglomeration exceeding 5.0100, it will show the dual effect of emission reduction and energy conservation. (4) Energy intensity could work as the intermediary variable of innovation agglomeration’s influence on carbon emissions. Through its various positive externalities, innovation agglomeration can produce a direct impact on carbon emissions, and through energy intensity, it can also affect carbon emissions indirectly.

As structural transformation and economic development have progressed, the issue of climate change has become increasingly critical, concurrently with the growing significance of women in the labor market. Yet, the impact of the gender gap in labor participation rates on carbon emission efficiency remains ambiguous. This article employs a data envelopment analysis model to evaluate carbon emission efficiency, utilizing US state-level data from 2010 to 2021 for an empirical investigation into the influence of the gender gap in labor market participation and unemployment rates on carbon emission efficiency. Overall, the narrowing of the gender gap in the labor market contributes to the improvement of carbon emission efficiency. The detailed findings are as follows: (1) diminishing the gender gap in labor force participation and unemployment rates can enhance carbon emission efficiency, a trend particularly pronounced in states that have adopted the Equal Rights Amendment and during the pre-COVID-19 era; (2) gender gap in labor market participation and unemployment rates affects carbon emission efficiency through skilled labor biased technological progress and income inequality and (3) the increase of innovation efficiency, saving rate and endowment structure can mitigate gender gap in labor market participation and unemployment rates’ effects on carbon emission efficiency.