Xujia Zhang

Strategic Emerging Industries (SEIs) are a critical driver of China’s green transition and high-quality development; however, the Ecological and Environmental Effects of Firm Entry and Exit remain insufficiently explored. Based on micro-level data of Chinese SEI enterprises from 2009 to 2023, this study employs kernel density estimation and a panel fixed-effects model to construct a five-dimensional ecological environment evaluation system under the PSDRP framework and to examine the spatio-temporal evolution characteristics of Firm Entry and Exit and their Ecological and Environmental Effects. The results indicate that SEI enterprises exhibit agglomeration in the Eastern Region and gradual diffusion toward the Western Region, with exit activities showing higher spatial concentration. Firm Entry generates stage-specific constraining effects on the ecological environment, whereas Firm Exit alleviates ecological Pressure and enhances Resilience. Significant regional heterogeneity is observed, forming a pattern of optimization in the Eastern Region, improvement in the Central and Western Regions, and greater adjustment challenges in the Northeast Region. This study provides empirical evidence for differentiated and coordinated industrial–environmental policy design.

The rapid flow of resources and participation of diverse entities in the information age drive the formation of innovation networks, yet how these networks evolve in strategic emerging industries—particularly in transitioning regions like Northeast China—remains underexplored. This study investigates the spatial formation and evolution of cooperative innovation networks in strategic emerging industries, as well as the underlying mechanisms driving their development and transformation. Focusing on China’s three northeastern provinces—a representative old industrial base undergoing transformation—we construct and analyze inter- and intra-city cooperative innovation networks from 2009 to 2023 using invention patent data. Employing social network analysis and spatial econometric models, we reveal that while the external network exhibits limited expansion with Beijing as a dominant hub, the internal network demonstrates increasing cohesion, transitioning to a multi-core structure. Factors such as urban innovation capacity, economic proximity, and institutional proximity play key roles in shaping these networks. These findings offer policymakers empirical evidence to strategically optimize cross-regional innovation ecosystems in resource-dependent regions, supporting urban sustainability and fostering innovation-driven development.

Plant absorption via aquatic plants is vital for the deep purification of treated wastewater. This study aimed to determine the removal efficiencies of nitrogen and phosphorus for different aquatic plants and the effect of monochromatic light as compared to white light. Five plants (i.e., Iris pseudacorus, Oenanthe javanica, Zantedeschia aethiopica, Ipomoea aquatica Forssk. and Sagittaria trifolia) were cultured in prepared wastewater and radiated by white, red, green and blue LED lamps with 8 h radiation per day, respectively. After 4 d of cultivation, the O. javanica and S. trifolia exhibited relatively better growth status and higher TP removal rates (90%). The blue light radiation played a key role in the TP uptake of the tested plants. The N removal rates of plants were relatively lower (10–40%), limited by the low COD content. The S. trifolia exhibited the highest efficiency, and red light promoted the removal of TN and NO₃⁻-N, whereas NH₄⁺-N removal was driven by blue light radiation. So, O. javanica and S. trifolia coupled with blue and red lamps as supplementary light were suggested for the deep purification of municipal treated wastewater. The effect of intensity and ratio of monochromatic lights could be a direction for further research.

To provide a scientific basis for pollution prevention and control of the urban environment, the current status of heavy-metal pollution in road dust in Harbin was studied. In total, 63 road dust samples in the main urban area of Harbin were collected, and the contents of Cd and Pb, two representative heavy metals, were detected. Using the Moran Index coupled with semi-variable function and geostatistical methods, the spatial correlation, variation structure, and distribution pattern were analyzed, and the estimated probability of the heavy metals exceeding the safety standard was determined. The study showed that: The mean concentrations of Cd and Pb were higher than the background values in Heilongjiang province, and both exhibited moderate variability, while the coefficient of variation of Cd was larger than that of Pb; Cd was weakly correlated in space and randomly distributed, Pb was moderately correlated in space and exhibited good spatial structure, and both were spatially aggregated. The optimal model for fitting the variance function showed that Cd was a spherical model, and Pb was an exponential model. The variation of Cd was mainly influenced by human factors, and the variation of Pb was influenced by both structural and random factors. The optimized interpolation results of the variance function had high accuracy, and the spatial distribution of Cd was elliptical, whereas the distribution of Pb was stripe-shaped, Cd was mainly influenced by traffic factors, such as industrial enterprise distribution and road painting, while Pb was influenced by natural factors such as river sediment or the study area belonging to a geologically high background area, in addition to the above factors. The estimated probabilities indicate a higher potential risk of Cd in the northeastern part of the study area.