In order to actively respond to the global climate and environmental challenges, and to help achieve the goal of carbon peaking and carbon neutrality, China aims to achieve carbon peaking by 2030. As the main contributor of energy consumption, construction industry transformation is imminent. This paper focuses on the development trend of carbon emissions in Anhui construction industry in the next 20 years, and how to help Anhui construction industry achieve the carbon peak target. The research process and conclusions are as follows: 1) Through literature review and gray correlation analysis, seven important factors affecting the construction industry are identified. 2) The gray prediction EGM(1,1) model was adopted to forecast the carbon emissions of the construction industry in Anhui Province from 2021 to 2040. The research results show that carbon emissions will increase year by year in the next 20 years. 3) The EGM-BP neural network model was used to further predict the carbon emissions from the construction industry in 2021–2040. It is expected that the carbon emissions from construction industry in Anhui Province will show an “inverted U″ trend in the next 20 years, and is expected to reach its peak in 2031, after which it will start to decline. Based on the results of the above study, we provide support paths for achieving the carbon peak in the construction industry from the perspective of improving and developing the construction system, and provide policy suggestions for relevant departments to accelerate the construction industry and carbon peak construction in Anhui Province.

This paper focuses on the development trend of industrial carbon emissions in Bengbu city, Anhui Province in the next ten years, and how to help the industry reach the carbon peak as soon as possible. The research process and conclusions are as follows: (1) Through literature review and carbon emission index method, five main factors affecting industrial carbon emission are identified. (2) The resistance model is used to analyze the main resistance factors of industrial carbon emission reduction in Bengbu city. (3) Based on the existing data of Bengbu city from 2011 to 2020, the grey prediction EGM (1,1) model is used to predict the industrial carbon emissions of Bengbu city from 2021 to 2030. The results show that among the five factors, the urbanization rate has the most significant impact on industrial carbon emissions, while energy intensity has the least impact. Bengbu’s industrial carbon emissions will continue to increase in the next decade, but the growth rate will be flat. Based on the findings of the analysis, specific recommendations on urbanization development, energy structure, and industrial structure of Bengbu city are put forward.

Scientific evaluation of water ecological security has essential theoretical and practical values for protecting and restoring the Huaihe River aquatic ecosystem. Based on the DPSIR-EES model, the water ecological security index (WESI) system of 16 cities in the Anhui section and Shandong section of the Huaihe River Basin was constructed. The ‘single index quantification-multi-index comprehensive-multi-criteria integration’ (SMI-P) evaluation method was used to calculate the WESI of the study area from 2011 to 2020. The spatial autocorrelation model and Markov chain were used to study the spatial distribution characteristics of water ecological security level and the probability of evolution over time. The results show that: (1) The overall WESI of the Huaihe River Basin has increased year by year, but the water ecological security level has been in the early warning (III) stage. Response level and environmental level are the main constraints. (2) During the study period, the spatial distribution of WESI in the Huaihe River Basin was high in the north and south ends and low in the middle. The degree of spatial agglomeration gradually changed from positive correlation to negative correlation. (3) The transfer of water ecological security level has ‘path dependence’ and, ‘self-locking’.

In this paper, the Hefei metropolitan area is selected as the research object to measure industrial carbon emissions in this area during 2010–2022. The main contribution is to deeply analyze the characteristics of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area with the modified gravity model and social network analysis(SNA), and to explore the driving factors of its formation with quadratic assignment procedure(QAP). It establishes the foundation for the Hefei metropolitan area to differentiated green city development policies. The results show that (1) The industrial carbon emission in the Hefei metropolitan area showed a fluctuating and rising trend, forming a strongly connected carbon emission network spatial patterna; (2) The overall structure of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area is stable, but the tightness of the spatial correlation relationship among cities is not enough and the hierarchy is evident; (3) Hefei and Huainan are the centers of the carbon emission spatial correlation network of the Hefei metropolitan area, the position of each city in the carbon emission spatial correlation network is obviously unbalanced; (4) Urbanization level, economic development level, technological innovation, level of opening up and industrial structure exert considerable influence on the establishment and evolution of the spatial correlation network pertaining to industrial carbon emissions within the Hefei metropolitan area.

Ecological compensation is an effective way to alleviate watershed water ecological management. Considering the behavior of public participation, this paper constructs a tripartite evolutionary game model of the public, enterprises, and local governments, analyzes the evolutionary stability of the strategic choice of each participant, and discusses the influence of various factors on the strategic choice of the three parties. Combined with regional data, Matlab R2018b is used to simulate and analyze the evolution trend of each subject strategy in the Huaihe River Basin under different situations. The study found that: (1) The government's increase in rewards and punishments has significantly promoted public participation and enterprises' active governance of pollution, but increasing rewards and punishments are not conducive to local governments' own performance of regulatory responsibilities. (2) Increasing the amount of compensation and incentives is an effective way to promote public participation. (3) The sum of reputation gains, reputation losses, and rewards and punishments is greater than the difference between the additional benefits of negative governance and the cost of corporate governance, so as to ensure that enterprises actively control pollution.

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.