Industrial symbiosis (IS) is a key sub-field in industrial ecology. The field itself assists in developing strategies that support local and regional sustainable development. The evolution of IS is an important topic to be explored. In this paper, we use the system dynamics (SD) method to establish a model of the evolution of industrial symbiosis in the case of Hai Hua Industrial Symbiosis (HHIS). In this model, different scenarios for energy consumption, solid waste utilization, and wastewater utilization were set by changing the parameter values of certain input variables to analyze the evolution of HHIS. In addition, the drivers for IS evolution were investigated qualitatively in this study. The SD model was helpful for visualizing the effects and benefits of reducing the energy consumption, solid waste stock, and wastewater stock that were achieved by establishing symbiotic relationships during the evolution of IS. The results show that the optimization of energy and material flows and other symbiotic benefits can be achieved in HHIS. There are still some challenges that restrict the further evolution of HHIS. Some suggestions are proposed to promote its further evolution.

Under China’s “dual-carbon” goal, it is necessary to coordinate the relationship between economic growth and emission reduction. Based on the panel data of 30 provinces in China from 2011 to 2021, this paper explores the coordination level among carbon cutting (CC), pollution reduction (PR), and economic growth (EG) by using the coupling coordination degree (CCD) model, a cold and hot spot analysis, and the Dagum Gini coefficient. Furthermore, we analyze the influencing factors of CCD from a spatial perspective using the geographically weighted regression (GWR) model. The results show that the coordination level of CC, PR, and EG in China has continued to improve and entered a moderately coordinated stage. Meanwhile, regional differences are also evident. The eastern region is a high-CCD concentration area, while the northwest and northeast regions are low-CCD concentration areas. However, inter-regional differences in CCD are decreasing. Urbanization, foreign direct investment, and new quality productive forces contribute significantly to achieving synergies among CC, PR, and EG. However, the effect of industry digitization on CCD fails the significance test in most provinces. The effects of the factors on CCD exhibit obvious spatial heterogeneity characteristics. These findings can provide an important basis for the formulation of regionally differentiated green and low-carbon development policies.

Understanding the synergistic effects of pollution reduction (PR) and carbon mitigation (CM) and their driving factors is essential for achieving environmental improvement and dual-carbon targets. On the basis of panel data from 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2010 to 2023, this study analyzed the spatiotemporal evolution of air pollutant and carbon emissions. The synergistic effects of PR and CM were quantified using the co-control effect coordinate system and vector angle analysis, and their underlying driving mechanisms were examined using a geographically and temporally weighted regression model. Results showed that air pollutant emissions in the BTH region declined substantially over the study period, whereas carbon emissions increased in all cities, except Beijing. The spatial patterns of air pollutant and carbon emissions were largely consistent, with Tangshan being a high-emission hotspot and northern Hebei cities being low-emission areas. Most cities were in a “pollution reduction but carbon increase” stage, but the overall synergistic degree gradually improved. The synergistic effects were positively driven by green travel and technological R&D and negatively influenced by economic development, energy utilization, and transportation structure. The positive effect of industrial structure on PR and CM weakened, and spatial heterogeneity was evident. Economic development and technological R&D exerted strong influences in southern Hebei. Energy utilization and transportation structure had pronounced effects in northern Hebei. Industrial structure had remarkable effects in cities surrounding Beijing and Tianjin. Moreover, green travel demonstrated spatial heterogeneity, exerting a facilitative effect on emissions in southern Hebei cities. These findings provide policy implications for promoting the synergistic effects of PR and CM in the BTH urban agglomeration.