Research on the impact of environmental regulation on high-quality economic development

The Nonlinear Impact of Environmental Regulations on Urban Employment: Evidence from the Yangtze River Economic Belt of China

As an important policy tool for promoting green development, environmental regulation (ER) has exerted a profound impact on the employment sector. Using panel data from 106 prefecture-level and above cities in China’s Yangtze River Economic Belt (YREB) from 2011 to 2022, we examine the impact and mechanisms of ER on employment scale and structure. Empirical results reveal that the strengthening of ER has a significant inhibitory effect on employment growth, but a positive impact on the optimization of the employment structure. As regulatory intensity increases, the suppressive effect on employment scale diminishes, while structural optimization strengthens. Moreover, the effects vary across regions. The scale-inhibiting effect is more pronounced in the Upper-Middle Yangtze regions, in areas with initially low regulation intensity, and in large cities. Structural optimization is most significant in the Lower Yangtze regions and in small/medium-sized cities. We also find that ER indirectly changes employment through three channels: industrial structure upgrading (ISU), green innovation (GV), and foreign direct investment (FDI). Our findings underscore the necessity of balancing environmental stringency with social sustainability, and the conclusions of our study are applicable to policymakers in China and other economies.

Measuring the Development Level and Influencing Factors of Green Finance under the Context of a Low-Carbon Economy

Green finance (GF) is increasingly recognized as a critical instrument for facilitating the low-carbon economic transition. This study utilizes panel data of 30 Chinese provinces from 2015 to 2024, comprising 300 observations. First, an index system for assessing the level of green finance (GF) development is established using the entropy-weighting method. Subsequently, fixed-effects model is applied to examine the direct effects of per capita GDP, industrial structure upgrading, and the intensity of environmental regulation on GF development. To assess the robustness of the findings, a series of robustness checks are conducted. In addition, regional heterogeneity is examined by comparing the effects across eastern, central, and western China. The findings indicate that environmental regulation intensity exerts a substantial and robust positive effect on GF development; the negative impact of industrial structure upgrading aligns with expectations but fails to reach statistical significance; and the effects of openness to foreign trade and the share of fiscal expenditure on environmental protection are not significant. Regional differences present a more complex picture. In the eastern region, GF development is jointly driven by environmental regulation and market forces. In the central region, economic development and R&D investment play a more important role, while environmental regulation remains the dominant driving force in the western region. The study, therefore, after the analysis, suggests policy measures including promoting substantive industrial upgrading, strengthening environmental regulatory frameworks, enhancing interregional cooperation, and improving green financial market mechanisms. The findings provide empirical evidence for advancing high-quality GF development. They also contribute to achieving low-carbon economic goals.

The Impact of Institutional Quality on 𝑪𝑶₂ Emissions in Developing Economies

Digital empowerment for green governance: digital economy, public environmental concern, and urban industrial pollution control

The digital economy (DE) is a nascent economic model with the potential to mitigate urban industrial pollution (UIP) while encouraging high-quality economic expansion. This paper utilized panel data from 258 Chinese cities and employed the fixed-effects model, the mechanism test model, panel threshold model, and the spatial Durbin model to undertake an empirical investigation into the DE on UIP emissions. The research outcomes suggest that developing the DE has a positive influence on UIP reduction and that the effect of the DE on UIP reduction varies significantly across regions with different levels of industrial development, digital infrastructure, and environmental regulatory intensity. Further examination shows that the DE can reduce UIP by promoting public environmental concern and green technology innovation. Additionally, when the DE is used as a threshold variable, the DE’s impact on UIP emissions exhibits a nonlinear double-threshold effect, with two key turning points of 0.0921 and 0.5515, which constitute a three-stage model of “weak effect—strong effect—weak effect”. When human resource leve is used as a threshold variable, the impact of the digital economy on reducing urban industrial pollution is gradually increasing and exhibits a nonlinear single threshold effect. Under different geographical and economic distance spatial weight matrices, the DE’s development has a substantial negative spatial spillover effect on UIP emissions. That is, developing the local DE can directly reduce local UIP and significantly suppress UIP in neighboring areas indirectly through information sharing and technology diffusion. The findings of this study provide important management insights for government managers to advance environmental governance and promote green development.

Uncertainty as a catalyst? The dual role of economic policy uncertainty in green technology innovation in China

As China pursues high-quality, innovation-driven growth, it faces rising economic policy uncertainty (EPU) fuelled by information gaps, changing policy priorities and geopolitical tensions. Yet how EPU shapes green technology innovation remains contested. Analysing panel data from A-share listed companies (2009–2019), this study reveals that EPU motivates enterprises to improve green innovation levels. Mechanism analysis shows that EPU increases R&D investment and strengthens environmental, social and governance (ESG) performance, especially among large and state-owned enterprises, while significantly reducing the effectiveness of environmental subsidies. These findings suggest EPU acts as a strategic catalyst, though patent growth may partly reflect strategic filing behaviour rather than substantive technological progress. Consequently, policy frameworks must provide predictable, long-term support. Governments should improve subsidy mechanisms and promote adaptive R&D strategies to utilize uncertainty as a driver for green transformation. While the 2009–2019 scope ensures data reliability, the results’ external validity should be considered alongside post-2020 macroeconomic and geopolitical shifts.

Disentangling the drivers of green total factor productivity growth: how environmental protection tax acts through pollution reduction, factor allocation, and digital intelligence

Within the context of high-quality development imperatives, understanding mechanisms through which environmental regulation elevates corporate green total factor productivity (GTFP) represents a critical research priority. Adopting an endogenous policy shock lens, this study examines Chinese A-share listed firms spanning 2013–2023. Leveraging the 2018 implementation of China’s Environmental Protection Tax Law as a quasi-natural experiment, we employ a difference-in-differences (DID) specification to systematically assess how environmental taxation influences corporate GTFP, while investigating the moderating roles of artificial intelligence (AI) adoption and digital transformation (DIG) processes. The study reveals three main results. First, the environmental protection tax substantially increases GTFP among heavily polluting firms. This improvement mainly comes from better pollution reduction performance and is driven by green technological advancement rather than gains in operational efficiency. Second, the positive impact of the policy is stronger in industries with intense competition and in firms that receive limited attention from financial analysts. Third, the adoption of AI and digital transformation markedly reinforces the beneficial effects of the tax. These results confirm that the tax boosts GTFP primarily by encouraging technological progress through end-of-pipe pollution control measures. The article offers three fresh contributions to the literature. On the theoretical front, it uses a dual decomposition approach, separating input–output changes from shifts in technology and efficiency, to clearly distinguish between pollution-abatement effects and factor-reallocation effects. This uncovers how the environmental tax promotes GTFP via technology upgrades rooted in end-of-pipe solutions, offering a novel analytical framework for examining the Porter hypothesis at the firm level. Next, the study introduces AI and digital transformation as key moderating factors in the relationship between environmental regulation and GTFP. It demonstrates that digital technologies act as powerful amplifiers of policy effectiveness, opening up promising new avenues for research at the intersection of environmental rules and digital innovation. Finally, the analysis examines how the policy’s impact varies depending on the degree of market competition and the level of external information oversight. These insights provide useful guidance for designing more tailored environmental policies. From a practical perspective, the findings can inform the setting of differentiated tax rates, the development of supportive policies that leverage digital tools for green transformation, and the implementation of targeted measures suited to specific industries and firms with different information environments. Overall, the analytical framework and empirical evidence developed here offer a replicable approach and research template for future studies on green innovation, policy coordination, and digital empowerment, thereby creating potential multiplier effects in the academic community.