This study demonstrates algorithms that assist municipal administrations to make the best environmental decisions. The algorithms developed by large alpha-class municipal governments with assistance of department of environmental agency data analyst. Mathematical and econometric modeling techniques as well as optimum solution theories adhered to develop a model, and the criteria is functionality, which reflects a balance between maximum profit, comfort in living circumstances, the environment, and the need to avoid a market failure scenario. The ensuing results allow for the most optimal administrative decisions, such as the rate of environmental taxes. The empirical findings show that higher environmental, social and governance performance and digital finance has improved the corporate financing efficiency, as well as the influence of ESG performance on energy efficiency, all at a 1% significance level.

The purpose of the study is to test the role of energy development in energy financing considerations for sustainable energy innovation and financial development. To achieve the study objective, a fuzzy decision-making modeling technique is applied. The results revealed that bank loans are now the main source of financing for innovation and creativity in Chinese business entities. Project-based financing might be replaced with collaborative and sustainable energy innovation (CSI), warranting energy development. Moreover, green financing loan schemes invest both public and private funds in sustainable energy innovation to capitalize on financial development through sustainable energy innovation. The consideration and application of financial consideration for sustainable energy innovation-financing projects or companies are limitless. Providing for screening energy development cooperation proposals with small financial payback hurdle rates might have large opportunity costs. There may be a case for governments to increase industrial growth, improve resource efficiency, and increase factor productivity while tackling climate change. Economic growth in China may have an even greater influence on environmental sustainability than in other countries. On such points, there is a need to pay the attention. If the suggested policy suggestions are implemented successfully, they would help to enhance the scope of financing considerations for sustainable energy innovation to uplift financial development through energy development mechanisms at the corporate level.

Technological singularity has seriously affected all the social, environmental, and economic genesis factors in mankind’s history. The problem of assessing the quality of life in the digital economy is acquiring new nuances including social services. The objective of this study is to investigate unique impacts of digital transformation on economic, environment, and social progresses on the quality of life improvement in China. Environmental statistical data on the impact of investments in social, state, and other spheres of economic activity are analysed at the machine learning level. Application of high-performance computing (HPC) and big data technologies for obtaining data on socio-economic statistics in real-time, the presence of feedback in the Web 4.0 concept, transfer of a significant part of economic processes to internet platforms provide the information necessary for analysis. As a result, a basis is proposed for implementing software products in the form of institutional decision-making support systems for a long horizon of planning investments in the quality of life.

The study of mountain areas has always received great attention from science. However, the lack of a unified model for the development of mountain areas leads to a variety of recommendations that may not always be consistent. To achieve sustainable development, it is necessary to conduct a comprehensive assessment of the natural resource potential and level of economic development of the analyzed territory. The object description is an m-dimensional vector, where m is the number of signs used to characterize the object, with the j-th coordinate of this vector equal to the value of the j-th feature, j = 1 ,. .., m. In the description of an object, the absence of information about the meaning of a particular feature is permissible. The combination of a certain number of objects and their attributes is a sample on which n algorithms (proposed development models) have been worked out. The quality of operation of each algorithm is assessed (the model is estimated by the Boolean function). None of the algorithms considered performed perfectly on all the set of specified objects. A logical method is proposed for constructing a new algorithm (correction model), which is optimal on the entire set of recognized objects. The result of the study is the optimal model which includes the positive properties of the previously considered models and corrects their shortcomings. The proposed approach may be the basis for obtaining expert assessments and recommendations in order to build an optimal strategy for the development of mountain areas.

Investigated are the effects of digital finance on green bonds and renewable energy. Therefore, the primary objective of this research is to employ a novel time-varying causality test to establish the causal link between green technology, clean energy, digital finance, and environmental responsibility. Study analyzed data from 2001 to 2019 to infer the China region. In addition, for robustness, a spillover dynamic connectedness model is implemented. The empirical results show that the spillovers shocks analysis come from clean energy to digital finance index(30.544%), followed by propagation from clean energy to green economic index (30.544%). Because depending on economic events, the dynamic total connectedness across assets changes over time. Long-term Environmental costs are dramatically reduced by 0.68% with every 1% increase in clean energy consumption. Yet, the entire period from clean energy to digital finance is marked by heightened volatility and causal relevance. The study finds that after the local economy and environmental governance, institutional environment has the second-largest impact on the market expansion for green bonds. The findings add to our understanding of the risk profile of clean energy stocks and emphasise the need for stable, predictable laws in order to increase the marketability of clean energy stocks.