Green innovation integrates innovation-driven and green development strategies, which helps to realize the green transformation of production and life in the Yangtze River Delta region, and promote high-quality economic development. Based on the multidisciplinary cross attribute of system dynamics (SD), the boundary and influencing factors of the green innovation system are defined, and the system dynamics model of green innovation ability of science and technology enterprises is constructed. With the help of statistical data from 2010 to 2020, the model is simulated to explore the change trend and law of elements. The results show that: (1) The green innovation ability of science and technology enterprises is composed of three stages, knowledge innovation, technological innovation, and innovation application, which are interconnected and progressive. The change trend of each variable is conducive to the improvement of green innovation competitiveness, and the green innovation benefits are significant. (2) Green innovation is driven by multidimensional factors such as R&D investment, technological innovation investment, knowledge innovation ability, and the conversion rate of scientific research achievements. The improvement of the conversion rate of scientific research achievements has the greatest impact on the enterprise’s green innovation ability, and the change trend is more obvious. (3) Positive and negative two-direction sub-mode regulation of R&D investment, technological innovation investment, and scientific research achievement conversion coefficient will affect the speed of green innovation accumulation of enterprises, and this increment is marginally increasing with the increase of the coefficient in the short term. Finally, some suggestions are put forward to promote the green innovation ability of science and technology enterprises in the Yangtze River Delta.

The security pattern of the ecosystem in Huaihe River Economic Belt plays a vital role in maintaining the healthy and stable structure of the ecosystem and the green development of the ecological environment in the central and eastern regions of China. This study takes Anhui section of Huaihe River Economic Belt as the case studies, and then the Pressure-State-Response (PSR) theory was used to construct the ecosystem security evaluation index system. In addition, cloud matter element as well as resistance diagnosis model was adopted to dynamically evaluate the spatio-temporal evolution pattern. The final stage was to diagnose main obstacle factors of ecosystem security in Anhui section of Huaihe River Economic Belt from 2010 to 2018. The results indicate that: (1) Overall, the ecosystem security level of Anhui section of the Huaihe River Economic belt has undergone the evolution trend of “descending - ascending - descending - ascending”, and the overall change shows the curve of “W” shape. (2) From the perspective of spatial evolution, the ecosystem security level of southern cities is higher than that of northern cities. (3) The main obstacle factors are urban population density, the proportion of energy conservation and environmental protection expenditure in fiscal expenditure, per capita urban road area, and industrial wastewater discharge, etc. Therefore, according to the regional development differentiation strategy, it is necessary to actively advocate the development mechanism of banning new construction, closing pollution sources, and optimizing structure as well as building new green, so as to further promote the safe and healthy operation of the ecosystem in Anhui section of the Huaihe River Economic Belt.

This paper takes Anhui section of the Huaihe River Economic Belt as the research object, and comprehensively uses SBM-Undesirable and DEA-Malmquist models to measure the industrial eco-efficiency from 2009 to 2018. With the help of ArcGIS and GeoDA software, the spatial characteristics of industrial eco-efficiency are analyzed. Based on Tobit panel regression model, the paper makes an empirical analysis on the influencing factors of industrial eco-efficiency, and discusses the mechanism of industrial eco-efficiency pattern evolution. The results indicate that: (1) On the whole, the level of industrial eco-efficiency from 2009 to 2018 is relatively low, and the difference of industrial eco-efficiency among eight cities is obvious. Spatially, the eco-efficiency index shows an increasing spatial pattern from north to south. (2) Most of the eight cities in Anhui section are in the input-output development mode of high benefit, medium consumption, low pollution and low benefit, low consumption and low pollution. (3) Economic development level and research innovation intensity have a positive promoting effect on industrial eco-efficiency. Urbanization level and the proportion of environmental protection investment have a negative inhibiting effect on the improvement of industrial eco-efficiency. The industrial structure and the degree of openness have no significant effect on industrial eco-efficiency.

Promoting industrial ecology and greening is the fundamental requirement of the new development concept, and also the key to realizing high-quality and sustainable development of regional economy. This paper selects the relevant data of eight cities in Anhui section of Huaihe River Economic Belt from 2014 to 2018, and uses the entropy method and the undesirable SBM model to calculate the industrial development level and ecological efficiency during the study period. Moreover, based on the coupling coordination degree model, the synergistic effect between industrial development and ecological efficiency is analyzed, and the spatial-temporal evolution pattern between them is clarified by means of spatial econometric analysis model. Further research suggests that: (1) the synergistic effect between industrial development and ecological efficiency in Anhui section of Huaihe River Economic Belt from 2014 to 2018 is significant, showing a steady strengthening trend. The endogenous power of industrial development keeps strengthening, and the scale effect keeps spillover diffusion. The mean value of overall coupling coordination degree is generally between 0.6 and 0.7. (2) In terms of spatial distribution, the regions with high coupling coordination degree are mainly distributed in the eastern part of Anhui Section (Bengbu and Chuzhou), the lower level area is mainly distributed in the northern part of Anhui Section (Suzhou), and the middle level area is widely distributed. (3) The agglomeration of coupling coordination degree between industrial development and ecological efficiency mainly occurs in low-high (L-H) and high-low (H-L) agglomeration areas. (4) The level of economic development, intensity of R&D and intensity of environmental regulation have significant positive effects on the coupling coordination degree of industrial ecological efficiency and industrial development, and promote the increase of coupling coordination effect. Finally, based on the research results, targeted suggestions are put forward in order to promote the coupling coordination between industrial development and ecological civilization construction, and walk out of a new road of high-quality development of green ecology.

To deeply explore the risk factors and evolution paths contained in coal mine accidents and promote the transformation and upgrading as well as high-quality development of the coal industry, this paper adopts text mining and safety risk topic modeling to subdivide the safety risk system of coal mining enterprises into five subsystems: human resource allocation, establishment of scientific research and innovation platforms, technological innovation, policy support, and enterprise system construction. The network analytic hierarchy process and CRITIC weighting method are applied to calculate the comprehensive weights of each risk factor. Taking a certain coal mine in Qinghai as the research object, the system dynamics model is used to construct the causal loop diagram and stock-flow diagram of the safety risk resilience system of coal mining enterprises, and to simulate and analyze the impact of each risk factor on the overall safety risk resilience level of the case coal mine. The key technological innovation factor parameters are regulated to analyze the sensitivity changes of the safety production rate and safety risk resilience level of coal mining enterprises under different scenarios. The results show that: (1) The technological innovation and human resource allocation subsystems have the greatest impact on the safety risk resilience of coal mining enterprises, followed by the enterprise system construction, scientific research and innovation platform establishment, and policy support subsystems. (2) Increasing investment in safety, such as talent cultivation and technological innovation, can enhance the safety risk resilience level and shorten the time required to reach the expected safety value. Conversely, it will reduce the safety risk resilience level and delay the time to reach the expected safety value. (3) Further simulation and evaluation of the technological innovation subsystem reveal that the factors influencing the safety risk resilience of coal mining enterprises in order are construction survey and monitoring, support structure, construction design, mining plan, and engineering technology application. (4) The change trend of the conversion rate of technological innovation investment to engineering technology application and construction survey and monitoring is generally in a logistic “S” shape, mainly due to the time delay and lag of the benefits generated by safety investment.