In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.

To tackle the increasingly severe environmental challenges, including climate change, we should pay more attention to green growth (GG), a path to realize sustainability. Human capital (HC) has been considered a crucial driving factor for developing countries to move towards GG, but the impact and mechanisms for emerging economies to achieve GG need to be further discussed. To bridge this gap, this paper investigates the relation between HC and GG in theory and demonstration perspective. It constructs a systematic theoretical framework for their relationship. Then, it uses a data envelopment analysis (DEA) model based on the non-radial direction distance function (NDDF) to measure the GG performance of China’s 281 prefecture level cities from 2011 to 2019. Ultimately, it empirically tests the hypothesis by using econometric model and LightGBM machine learning (ML) algorithm. The empirical results indicate that: (1) There is a U-shaped relationship between China’s HC and GG. Green innovation and industrial upgrading are transmission channels in the process of HC affecting GG. (2) Given other factors affecting GG, HC and economic growth contribute equally to GG (17%), second only to city size (21%). (3) China’s HC’s impact on GG is regionally imbalanced and has city size heterogeneity.

Given the increasingly serious ecological and environmental problems in China, research on enterprises’ low-carbon sustainable development behavior (LCSDB) has become a heated discussion. This is also because enterprises are a primary source of carbon emissions and environmental pollution. From the perspective of the board of directors’ capital (BODC), this study considers empirical evidence from 286 enterprises listed on the Shanghai and Shenzhen stock exchanges in China from 2008 to 2016 to examine the BODC’s impact on enterprises’ LCSDB and its mechanisms. A group test is conducted using the enterprise’s property, nature of rights, and region, among other factors, to investigate the heterogeneity of the impact of board capital on enterprises’ LCSDB and its regulatory role. The research indicates (1) an increase in BODC promotes enterprises’ LCSDB. (2) An awareness of social responsibility (AOSR) plays an intermediary role in the relationship between BODC and corporate LCSDB. (3) Media attention enhances the BODC’s role in promoting enterprises’ LCSDB. (4) Government regulatory factors promote the BODC’s positive impact on LCSDB. These findings significantly impact the effectiveness of decision-makers within the company, the governance mechanism to address climate change risks, and the possible connection between corporate governance reform and carbon-related policies.

Global warming resulting from greenhouse gas emissions has been a worldwide issue facing humanity. Simultaneously, governments have the challenging task of striking a judicious balance between increased economic growth and decreased carbon emissions. Based on the energy-environment-economy triple coupling (3E-CGE) model, we endogenously integrate climate-friendly technologies into the model’s analysis framework through logic curves and refine and modify the CGE model’s energy use and carbon emission modules. We conduct a scenario simulation and sensitivity analysis on carbon tax, carbon-trading, and climate-friendly technological progress, respectively. The results reveal that carbon tax and carbon trading contribute to reducing carbon emissions in the short-term but achieving the goals of peak carbon and carbon neutrality will cause the collapse of the economic system. In the long-term, climate-friendly technologies are key to achieving the dual carbon goal; the development of such technologies can also stimulate economic development. The best path for China to achieve its dual carbon goals and economic development in the next 40 years involves effectively combining the carbon tax, carbon trading, and a climate-friendly technological progress. Specifically, China can begin trading carbon in high-emissions industries then impose industry-wide carbon taxes.

Green credit is an indispensable funding source through which China can achieve its carbon neutrality goal. This paper quantifies the influences of different green credit scales on energy structures, carbon reduction, the industrial economy, and the macroeconomy. It creates a green credit mechanism related to green technology innovation in a Chinese carbon neutrality computable general equilibrium (CGE) model and integrates energy, environmental, economic, and financial (3EF) systems. The green credit scale can influence green technology innovation and hence CO2 emissions. The results show that (1) green credit can accelerate China’s achievement of its carbon neutrality goal, and the larger the green credit scale, the less time it takes to achieve goals; (2) the influence of green credit scales confers marginal decreasing effects with realistic policy considerations; (3) using a cost–benefit perspective, 60% is the most appropriate green credit scale to use to achieve dual carbon goals in China; (4) the different green credit scales have a heterogeneous impact on the industry output, and high-carbon-emission producers from nonenergy industries need to pay attention to their green credit risk. This research provides a scientific reference for the policy design of China’s future green financial market development.

As the grassroots-party organizations of the Communist Party of China (CPC) are increasingly involved in the governance of private-owned enterprises (POEs), whether this new pattern promotes corporate innovation is still a research gap. Therefore, based on the data of 1357 POEs’ party-organization involvements and their patent applications from 2003 to 2017, this paper analyzes the impact of the party-organization involvements on corporate innovation by using the multiple regression model. The results include: (1) party-organization involvements including party organization activities and senior executives’ participation can significantly promote innovation, especially after 2012; (2) party-organization activities improve innovation by increasing research and development (R&D) investment and reducing operating risk, while the senior executives’ participation only influences on R&D investment; (3) the party-organization involvements have a stronger promotion on non-invention patent applications, especially for the utility-model-patent applications, than invention-patent applications; (4) the promotion is more pronounced for family businesses, technology-intensive and capital-intensive enterprises, as well as those located in the northern, Beijing-Tianjin-Hebei region and Yangtze River delta. After applying PSM sampling and difference-in-differences (DID) analyses, and substituting the dependent variables, the results remain robust. This paper provides Chinese evidence for party construction and corporate innovation, and also provides references about political connection and corporate innovation for other countries to some extent.