Evaluation mechanism of sustainable development level for the cities in the Yangtze River Economic Belt under the dual-carbon policy.

Low carbon sustainable development (LCSD) has become an inevitable choice, for which China has put forward a "dual-carbon" policy. The purpose of this study is to capture the interaction between the environment and the economy in the context of this goal, thus evaluating LCSD level from a systematic perspective. This paper proposes a super slack based measurement (SBM) model with a non-equal weight structure to assess the LCSD level. Firstly, a maximum influence minimum redundancy (MIMR) index selection algorithm is designed to establish input and output index systems, which avoids redundancy indexes. Secondly, the objective function of the original super SBM employs an equal weight structure, which leads results inadequately reflect the research preferences. Therefore, the weights of indexes are introduced to form an improved super SBM. Finally, 40 cities along the Yangtze River Economic Belt (YREB) are selected for empirical analysis. Results show that (1) the LCSD level of YRBE decreases from downstream to upstream to midstream; (2) Jiangsu, Zhejiang, and Sichuan provinces have higher LCSD levels, while Hunan and Jiangxi provinces have lower levels; and (3) up to 2021, there are 32 effective cities and 8 ineffective cities. The research implies that balancing the economy-environment relationship is crucial for higher efficiency. The LCSD evaluation method not only reflects the coordination level between the economy and the environment, but also integrates the research preference into the results, providing decision support for the government to formulate carbon reduction policies and allocate resources.

Fractional grey unequal-interval time-varying Lotka-Volterra model and its application for microbial communities in compost.

Aerobic compost is an effective method for the treatment of livestock manure, which is usually accompanied by complex interspecific competition. Describing these competitive relationships through mathematical models can help understand the interaction of microorganisms and analyze the effect of exogenous additive to regulate the composting process. The common model for analyzing competition problem is the Lotka-Volterra model. However, the fixed parameters of the Lotka-Volterra model are not suitable to reflect the dynamic variations of the competitive relationship when the environmental conditions change during composting process. Therefore, this paper establishes a novel fractional grey unequal-interval time-varying Lotka-Volterra model. Firstly, a fractional grey derivate operator is proposed on the basis of the unequal interval of composting data and historical dependence of microbial growth. Secondly, considering the influence of temperature, a time-varying parameter matrix is defined to reflect the variation of competitive relationship at different composting phases, and it is estimated by forgetting factor recursive least squares. Thirdly, the optimal coefficients are optimized by grey prediction evolution algorithm. Finally, the proposed model is employed to analyze the chicken manure composting experiment. The results show that the proposed model has lower error criteria and more accurate trend of fitting curve than the other five existing models. The parameter matrix describes the dynamical variation of microbial competitive relationship in two taxonomic levels and reveals that effect of the exogenous additive is principally reacted in the thermophilic phase and the competitive advantage is shifted from Bacteroidota to Firmicutes after treatment with the exogenous additive.

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis.

The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province's carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry's cleaner production and sustainable development of society.

Supplier selection mechanism in electric coal procurement under sustainability.

The electric energy mainly comes from thermal power generation fueled by electric coal, and the selection of electric coal supplier is of great practical significance for the development of electric power enterprises. In this paper, the mechanism of supplier selection in electric coal procurement is designed from a perspective of sustainability. Concretely, eleven evaluation indexes from three aspects of economy, environment, and society are selected to construct a new evaluation index system for selecting sustainable electric coal suppliers. Based on this index system, a new method of supplier selection in electric coal procurement based on 2-tuple correlation coefficient analysis is proposed. In this method, considering that the evaluation index values have the features of multi-source heterogeneous data, i.e., precise real number, interval number, and fuzzy linguistic variables coexist, all index values are converted into 2-tuples. Then, a 2-tuple deviation maximization method is proposed to determine the weight of each evaluation index, and a new method of 2-tuple correlation coefficient analysis is proposed to select sustainable electric coal suppliers. Moreover, the effectiveness and feasibility of the model are highlighted by an application example of selecting the electric coal suppliers. Combined with the development status of electric power industry and the research results of this paper, several countermeasures and suggestions are provided on how to formulate the optimal procurement strategy of electric coal for electric power enterprises and how to improve the competitiveness for electric coal suppliers.

Evaluation index system design and coordinated development analysis for the industrial green development system and regional non-waste system in the Yangtze River Economic Zone.

Based on the research background of the new economic norm and the construction of "non-waste city" in China, this paper studies the coordinated development between the industrial green development system and the regional "non-waste" system of the Yangtze River Economic Zone. First, using the data from 11 provinces from 2013 to 2017 as samples, the comprehensive evaluation index systems for the industrial green development system and the regional "non-waste" system are constructed, respectively. Then, considering the multi-source heterogeneous characteristics of the evaluation index data, the two-tuple linguistic entropy weight method, and gray relational analysis method are applied to filter the evaluation indexes and determine the index weights respectively. Third, combined with the dynamic TOPSIS idea, a new improved coupling coordination degree model is proposed to study the coordination development between the two systems. Finally, an empirical analysis is made, and the result shows that the overall degree of coupling coordination of the two systems shows a fluctuating upward trend. Moreover, the provinces are mainly low-level coupling coordination, and the factors that are hindered in various regions are different.

Influencing factors analysis and development trend prediction of population aging in Wuhan based on TTCCA and MLRA-ARIMA.

With the rapid development of the economy, the problem of population aging has become increasingly prominent. To analyse the key factors affecting population aging effectively and predict the development trend of population aging timely are of great significance for formulating relevant policies scientifically and reasonably, which can mitigate the effects of population aging on society. This paper analyses the current situation of population aging in Wuhan of China and discusses the main factors affecting the population aging quantitatively, and then establishes a combination prediction model to forecast the population aging trend. Firstly, considering the attribute values of the primary influence factors are multi-source heterogeneous data (the real numbers, interval numbers and fuzzy linguistic variables coexist), a two-tuple correlation coefficient analysis method is proposed to rank the importance of the influencing factors and to select the main influencing factors. Secondly, a combination prediction model named Multiple Linear Regression Analysis-Autoregressive Integrated Moving Average is established to predict the number and the proportion of aging population in Wuhan. By using the statistical data of Wuhan in the past 20 years, this combination prediction model is used for empirical analysis, and a prediction result of the number and the proportion of aging people in Wuhan in the future is obtained. Based on these quantitative analysis results, we propose some countermeasures and suggestions on how to alleviate the population aging of Wuhan from aspects of economic development, pension security system design and policy formulation, which provide theoretical basis and method reference for relevant population management departments to make scientific decisions.

Study on the interactive influence between economic growth and environmental pollution.

With the rapid growth of economy, the environmental pollution problem is becoming increasingly prominent. How to promote the coordinated and balanced development of economy and environment is a strategic problem of great significance that we face urgently. Taking Wuhan City of China as the research object, this paper selects the key indexes of economic growth and environmental pollution and studies the interactive influence between economic growth and environmental pollution in Wuhan. On the one hand, the impact of Wuhan's economic growth on environmental pollution is analyzed by the proposed time-delay correlation analysis method and the time-delay EKC (Environment Kuznets Curve) models. On the other hand, the impact of Wuhan's environmental pollution on environmental growth is studied. By establishing the LARS-LASSO (least angle regression-least absolute shrinkage and selection operator) regression model and the stepwise regression model, the main factors affecting economic growth in preliminary environmental pollution indexes are analyzed, and then, an interaction model is established to study the impact of the interaction between any two main environmental factors on economic growth. The results of empirical analysis show that the main factors affecting economic growth are industrial wastewater emissions, industrial waste gas emissions, and industrial smoke and dust emissions, and the interaction between industrial waste gas emissions and industrial wastewater emissions restrains economic growth.