Under the background of “peak carbon dioxide emissions” and “carbon neutrality” strategy, it is urgent to explore whether China’s great efforts to continuously promote industrial low-carbon transition can promote high-quality economic development. Taking the implementation of low-carbon industrial parks pilot policy (LIPPP) as a “quasi-natural experiment”, this paper tries to answer this question. The results show that the LIPPP doesn’t significantly promote the sustainable-oriented high-quality development (SHD) of the economy, but mainly boosts the value-oriented high-quality development (VHD), which is characterized by being obviously biased towards the technological progress. The policy effect is more obvious in the central region and cities with a relatively poor natural resource endowment. The mechanism analysis shows that the LIPPP promotes the VHD of the economy through the innovation incentive effect and the capital deepening effect. The technological progress related to the VHD promoted by the LIPPP has a crowding out effect on that of the SHD. In addition, regional innovation, capital deepening, and energy transformation all play a certain role in promoting the SHD, but which is overshadowed by the effect of the VHD promoted by the LIPPP. This paper provides policy implications for China to promote high-quality economic development in the process of low-carbon transition.

The nature of nonlinear dynamics of financial markets results in fractal geometry and chaotic behavior that can be viewed on a variety of scales in time. This paper conducts research on the fractal characteristics of the stock network and its contribution to the price of assets based on the Fractal Market Hypothesis (FMH). A multiscale network centrality measure is built based on high-frequency return dependencies to measure the self-similar, scale-invariant nature of inter-stock dependencies. The network factor and portfolio returns are then broken down with the wavelet packet decomposition (WPD) to obtain frequency-domain profiles, which characterize the variability of risk transmission in relation to investment horizons. The profiles are consistent with scaling properties of fractal, but the decomposition does not identify causal pathways on its own. Estimation of fractal dimension by use of the box-counting technique aided by the Hurst exponent analysis reveals that the A-share of China market exhibited long-range dependence and multifractal scaling. Network factor has the largest explanatory power in mid-frequency between the D5 and D6 bands of 32 to 128 days. This intermediary frequency concentration is consistent with the hypothesis of heterogeneous markets, in which the groups of investors with varying time horizons generate scale-related price dynamics. The addition of the network factor to a 6-factor specification lowers the GRS under the 5-factor specification by 31.45 to 17.82 on the same test-asset universe, indicating better cross-sectional coverage in the sample. The estimates of the Lyapunov exponents (0.039) as well as the correlation dimension ($D_2=4.7$) confirm the presence of low-dimensional chaotic processes of the network factor series, but these values are specific to the Chinese A-share market over the 2005–2023 sample period. These results provide a frequency-disaggregated use of network-based factor modeling and suggest that it can be applicable in multiscale portfolio risk management where the investor horizon is not uniform.

This study investigates the impact of integration into the international economic cycle (IEC) on green innovation in China’s manufacturing sector, a key factor in the country’s green strategic transformation. Using multi-regional input–output tables for both global and Chinese contexts from 2012 to 2017, alongside data from listed manufacturing firms, the analysis demonstrates that IEC integration significantly promotes green innovation in Chinese manufacturing enterprises. The mechanisms of innovation-driven development and the upgrading of production capital structure are central to this effect. Economic cycles involving Europe and developing economies exert a strong positive influence on green innovation, whereas demand from North America and East Asia has a comparatively weaker effect. State-owned and high-tech enterprises are identified as primary drivers of green innovation through IEC integration. The findings also indicate a high degree of dependence of China’s economy on the IEC. However, reliance on IEC integration alone may result in market failure, underscoring the essential role of government environmental regulation and macroeconomic guidance. The study provides valuable insights into the transformation and advancement of manufacturing and high-quality development within the context of the modernization of China.