Green development is considered to be an effective way to achieve win-win between economy and environment, and technological innovation is the core driving force of green transformation development. High-tech industries are the core industries of industrial technology and economic development, and their technological innovation ability and green development level are important supports for optimizing the national industrial structure and promoting the green transformation of industries. Therefore, based on the concept of innovation and green development, this paper analyzes the industrial development rule from the perspective of the interaction mechanism between technological innovation and green development, to provide theoretical reference for the design of corresponding development strategies and planning. Hence, NSBM model is constructed to evaluate the efficiency of multi-stage technology innovation and deconstruct the evolution law of technology innovation value chain of high-tech industry. Secondly, GML model is used to evaluate green total factor productivity and clarify the green and high-quality development status of high-tech industry. Finally, the truncated regression model is constructed to analyze the factors affecting the external environment of green transformation development. The results indicate that the upgrading of technology innovation value chain is conducive to promoting green transformation development.

Green and high-quality development has become an important development path for the global response to climate change. Eco-efficiency and innovation, as important indicator and core driver of green and high-quality development respectively, have attracted wide attention. However, the research on the relationship between innovation and eco-efficiency is mostly qualitative research, and the index selection of technological innovation and eco-efficiency is single. Therefore, this paper from the perspective of quantitative research based on the industrial enterprise data of 30 provinces from 2006 to 2019 to explore the mechanism between innovation network and eco-efficiency by constructing a three-stage evaluation model. The first step is to build the network SBM model to measure the innovation network efficiency, and decomposing it to explore the internal development and change rules of the innovation. And the SBM with undesirable output model is employed to evaluate the eco-efficiency and analyze the regional heterogeneity. Second, the coupling coordination model is constructed to quantitatively study the coupling coordination degree between innovation network efficiency and eco-efficiency. Finally, the influencing factors of coupling coordinated degree are discussed. The result shows that the innovation network comprehensive efficiency is lower than that of scientific and technological innovation efficiency and transformation of scientific and technological achievement efficiency, and the eco-efficiency of eastern China develops stably and is higher than that of the other three economic zones. Scientific technological innovation and the eco-efficiency of coupling coordination degree is higher than the transformation technological achievement and eco-efficiency of coupling coordination degree. Information and communication technology plays an important role in promoting innovation network comprehensive efficiency and eco-efficiency at different systems.

In order to further explore the internal transmission mechanism between technological innovation and green development in manufacturing industry under the background of obvious development characteristics in the new era, this paper constructed an integrated methodology system to evaluate the internal impact mechanism of technological innovation value chain efficiency on green development efficiency based on spatial perspective. First, the Network Slack-based model and Global Malmquist-Luenberger model are constructed to reveal the internal development law of technological innovation and green development of manufacturing industry. Secondly, the spatial Dubin model is employed to analyze the impact of current development characteristics and technological innovation on green development. The results show that innovation value chain efficiency is higher than technological innovation efficiency, and economic transformation efficiency is lower than that of technological innovation value chain. During the study period, the efficiency of technological innovation value chain in the four economic regions present fluctuant growth trend, and the eastern region has the highest value. The green development efficiency in the east, central, west, and northeast regions of manufacturing industry is higher than 1, and it shows an obvious spatial agglomeration effect. Besides, the efficiency of technological innovation, information and communication technology, urbanization, and the advanced industrial structure are all conducive to the improvement of green development in manufacturing industry. This paper studies the influence mechanism of technological innovation value chain efficiency on green development based on spatial perspective and puts forward relevant countermeasures and suggestions to effectively promote green development of manufacturing industry, providing relevant theoretical research for green and high-quality development.

Abstract High-tech industry has become an important position for international competition due to its strong technological innovation capabilities. Green development of the manufacturing industry is an important measure to respond to the profound changes in the global manufacturing industry, and technological innovation is the core force to drive the green development of the manufacturing industry. However, DEA model usually employed to measure the efficiencies of the innovation and green development, the internal transmission mechanism has not been thoroughly explored. In order to open the black box, this paper utilize the high-tech industry data to measure the efficiency of technological innovation process by using network Slack Based Measure (NSBM), Global Malmquist Luenberger index model (GML) is employed to evaluate and decompose the green development efficiency of high-tech industry, and the influencing mechanism between the efficiency of technological innovation and the efficiency of green development is explored by the spatial Dubin model. The results indicate that green transformation of the manufacturing industry has an obvious spatial linkage effect. In addition, technological innovation efficiency is conducive to the green development efficiency, while economic transformation efficiency and urbanization are not conducive to the green development efficiency.

Evaluating water quality characteristics (WQC) and tracing pollutant sources (PS) have gradually attracted worldwide attention. This study was conducted to develop an integrated method framework for evaluating WQC, tracing PS, and improving understanding of their relationship to efficiently managing the water environment. The single-factor index, comprehensive water quality index (CWQI), and hazard quotient and hazard index (HQ and HI) were used to evaluate the characteristics of single pollutant concentration, comprehensive concentration, and human health risk, respectively. These evaluation methods combined with relevant standards selected data from the original sampling data. These selected data were used for tracing PS by principal component analysis and Pearson correlation methods. 3384 sampling data were collected in the Yellow River Basin in 2021, and the WQC assessment and pollutant traceability were carried out by using the above-integrated method framework. The results showed that TN(total nitrogen) was the primary pollutant with an average concentration of 4.54 mg/L, followed by CODcr(dichromate oxidizability), NH4+-N(ammonia nitrogen), and TP(total phosphorous). The CWQI values ranged from 1.26 to 110.03, with an average of 7.74, indicating the pollution level of trace elements was excellent. The HQ and HI max values of As(arsenic) and Cr6+(hexavalent chromium) elements were over 1, meaning the elements have negatively affected local human health. Furthermore, the anthropogenic input was the primary pollutant source for TN. The anthropogenic input and agricultural source pollution emission could be considered for CODcr, NH4+-N, TP, and BOD5(five-day biological oxygen demand). The anthropogenic input and the weathering and leaching of loess could be considered for As elements. For Cr6+, F(fluorine), Anionic, and Petroleum, the anthropogenic activities were the primary pollutant sources, including the metal mining and production and the coal mining and processing industry. Our results could provide effective information to support adaptive management measures to improve water environment conditions and protect human health.

Ecological environment conditions (EEC) assessment plays an important role in watershed management. However, due to insufficient field data, EEC assessment in large-scale watersheds faces challenges. Our study was conducted to develop an effective EEC assessment method framework that was capable of reducing the use of field data. Three indicators were developed from multisource data, including landscape ecological risk index (LERI), road network density (RND), and industry density (ID). The knowledge-based raster mapping approach integrated the three indicators into an overall score of the EEC. Then model validation was conducted with principal components of water quality from field sampling data by Pearson correlation analysis methods. Finally, we applied and demonstrated the constructed method framework in the EEC assessment of the YRB.The results showed that bad EEC (0.5326 < Overall score <= 0.7679) areas were mainly distributed in the northern part of the YRB, showing a circular distribution pattern. The areas with bad EEC were 15.84 million km(2), accounting for 19.87 % of the YRB. The area of the highest LERI (0.157 < LERI <= 0.246), the highest RND (4.4435 < RND <= 8.5574), and the highest ID (0.1403 < ID <= 0.2597) finally converted to bad EEC was 7.22 million km(2), 0.78 millionkm(2), and 0.91 million km(2), respectively. The results indicated that the ecological risk factors were the primary challenges for improving EEC, followed by industrial agglomeration and road network factors. The primary factors affecting EEC varied between the provinces in the YRB, suggesting that provinces take the management strategies and measures should be adaptive. The correlation coefficients between EEC and the principal components of water quality characteristics were between 0.022 and 0.241, P < 0.05. These findings validated that our method framework could distinguish the spatial variation of EEC in detail and further provide effective support for watershed management.

Based on China's energy consumption structure, the reduction of energy intensity is conducive to the realization of China's carbon neutrality goal, and technological innovation is the core driving force for reducing energy intensity. This paper utilizes the statistical data of industrial enterprises in China during the period of 2011–2018 to explore the technological innovation value chain efficiency under the background of the new development stage. This study divides the innovation value chain into technological innovation stage and the economic conversion stage. The network slacks-based measure (NSBM) approach is applied to calculate the two different stages' efficiency. LSDV is utilized to analyze the influence mechanism of technological innovation factors on industrial energy intensity. The main results are as follows: first, the development trend of technological innovation efficiency (TIE) and the economic conversion efficiency (ECE) has heterogeneous effect across economic regions. The comprehensive development trend of innovation value chain in central China is the best among the four economic regions. Second, the effect of TIE on industrial energy intensity is negative, while the ECE is positive related to industrial energy intensity. In terms of technology spillover effect, the impact of import and FDI on industrial energy intensity is negative, respectively. The relationship between export and energy intensity is positive. The energy price has the negative relationship with industrial energy intensity, and the impact of energy price on industrial energy intensity is the largest. Through analyzing the efficiency of innovation value chain and the influence mechanism of technological innovation factors on energy intensity, this paper puts forwards relevant countermeasures and suggestions for effectively reducing industrial energy intensity and promoting high-quality industrial sustainable development. In addition, relevant theoretical research is provided for the sustainable green development.

Improving energy conservation efficiency is one of the prerequisites for China’s manufacturing industry to transform and upgrade. Jiangsu province which presents the maximum economic volume in manufacturing and its economic status in eastern China is comparable to Shanghai. Research on the sustainable development capacity of Jiangsu’s manufacturing industry gives important guidance for upgrading the manufacturing industry all over China. The core of China’s manufacturing transition to a manufacturing power is to enhance its independent innovation capabilities to improve energy efficiency and its position in the global value chain. Therefore, it is important to study the impact of technological factor on energy conservation potential and the transformation and upgrading of manufacturing. In this paper, multivariate regression research method combined with risk analysis is developed to explore the influence of the research and development factor on energy conservation while introducing macroeconomic variables. Additionally, energy conservation of manufacturing in Jiangsu province in 2020 and 2025 based on historical data from 1985 to 2015 is predicted. Compared with the business-as-usual scenario, the advanced scenario could reduce by 44.07 Mtce and 87.60 Mtce in 2020 and 2025, respectively. Thus, the results indicate that there is much room for improvement in terms of the energy efficiency for Jiangsu province.

Technical innovation promotes manufacturing industry to achieve energy conservation and emission reduction. As an area that accounts for more than 50% of China's economy, east region of China has taken the lead in developing and focusing on promoting industrial upgrading based on the innovation-driven development strategy. The potential solution of transforming China's manufacturing industry is to enhance the capability of independent innovation by increasing the investment in research and development. Research on the sustainable development capacity of east region manufacturing industry has been giving important guidance for upgrading the manufacturing industry all over China. However, few studies explore the research and development effect on the energy-saving potential of manufacturing at the regional level, and study the individual dynamic behavior to reflect industry heterogeneity. In this paper, the dynamic panel data research method is employed to explore the influence of the research and development on energy conservation while introducing macroeconomic variables. Additionally, block bootstrap-based scenario analysis is employed to predict the energy conservation of manufacturing in east region in 2020 and 2025 based on historical annual data from 1990 to 2016. Compared with the business-as-usual scenario, the advanced scenario could reduce the energy demand of 427.53Mtce and 1066.28 million tons of coal equivalent in 2020 and 2025, respectively. The results indicate that there is much room for improvement in terms of the energy efficiency for China's east region.

Driven by the transformation of the energy structure, China’s photovoltaic (PV) power generation industry has made remarkable achievements in recent years. However, there are more than 30 regions (cities/provinces) in China, and the economic, policy, technological, and the environmental conditions of each region are significantly different, which leads to a huge discrepancy in PV power generation efficiency. To address the imbalance in the development of PV industry, first, this paper employed the integrated fuzzy analytic hierarchy process–data envelopment analysis (FAHP–DEA) model to evaluate the PV power generation efficiency of 30 regions in China. Second, Tobit regression model was used to examine the effects of 9 potential influencing factors. Third, a concrete analysis was conducted, and discussion based on the efficiency rankings and regression results was made. Additionally, the FAHP–DEA model proposed in this study can also be applied to the efficiency evaluation issues of other types of renewable energy.

With the deregulation of power market and the increasing penetration of renewable energy, the core role of demand side management (DSM) has become even more prominent. In this sense, there is an urgent need for all market participants to identify the pivotal aspects of electricity market price fluctuation effectively and anticipate its future trend. For certain applications such as DSM, considering the high volatility and nonlinear of real-time electricity price, we can approximate the interval prediction to achieve the multi-classification that relies on critical pattern recognition of entire category of the price sequence. Therefore, this paper presents a study on the utilization of a novel electricity price classification framework which consists of Bayesian extreme learning machine (BELM) model, minimum redundancy maximum relevance (MRMR) algorithm, and multivariate sequence segmentation (MSS). Considering many advantages of deep learning structure in capturing the hierarchical and sophisticated characteristics of multidimensional sequence, the multi-layer BELM (ML-BELM) model is also extended and utilized to the modeling. To demonstrate the potential of the pattern classification framework, the proposed approaches are evaluated using hourly clearing price cases from Canada Ontario and New York electricity market. In particular, we investigate the performance of different classifiers regarding the 3 multi-classification and higher dimensional classification modes with respect to various scenarios in terms of precision, recall, AUC (Area Under roc Curve) score, and F1-measure indicators. The findings suggest that the proposed pattern classification framework can obtain satisfactory forecasting results provided that suitable scheme is utilized to the pattern segmentation and feature ranking process.

This study aims at exploring the relationship between renewable energy consumption and carbon dioxide emissions in China, and through the significance of renewable energy consumption, the hypothesis of environmental Kuznets curve at individual country level is tested as well as.Autoregressive distributes lag bounds testing approach is employed for empirical analysis.The results show that a quadratic relationship between renewable energy and CO2 emission has been found for the period support EKC relationship, and there exists a negative causality from renewable energy consumption to CO2 emissions.

In order to explore the impact of environmental regulation on the coordinated development of energy and the environment with the background of governance transition, we propose a three-stage integrated approach and use the panel data of China’s manufacturing industry 27 sub-sectors during the period of 2006–2015. In the first stage, according to the environmental pollution intensity, the manufacturing industry is divided into heavily polluting industry, moderately polluting industry, and lightly polluting industry. The second stage is employed the slacks-based measure (SBM)-undesirable method to study the sub-industries’ green energy-environmental efficiency under different environmental pollution intensities. Besides, the dynamic changes of technical innovation and efficiency among different industries are analyzed through the Malmquist productivity index. For the purpose of investigating the transmission mechanism of the Porter’s hypothesis and exploring the compound effects of environmental regulation and governance transition on green development, in the third stage, we use the panel data analysis to conduct more in-depth research on the relationship between environmental regulation, governance transition, and technical innovation. Results show that the highest average green energy-environmental efficiency is lightly polluting industry, which is 0.52, followed by the heavily polluting industry at 0.40, and the lowest is the moderately polluting industry, which is 0.32. By decomposing total factor productivity, heavily polluting industry is at the forefront of technical innovation. Panel data analysis results indicate that investment in research and development and governance transition could promote the growth of total factor productivity for manufacturing.

Distance and similarity measures have recently been investigated in-depth within the context of hesitant fuzzy sets. By analyzing the existing studies concerning distance measures for hesitant fuzzy sets, we find that they have some limitations. To address the flaws, this study develops some novel distance measures for hesitant fuzzy sets, including the normalized Euclidean distance measure, the Hausdorff metric distance measure, the normalized generalized distance measure, and their corresponding weighted distance measures. The proposals of this study not only hold many ideal characteristics but also do not consider the lengths of hesitant fuzzy elements as well as the arrangement of their possible values. To deal with the situations where both of the universe of discourse and the weight of element are continuous, some continuous hesitant fuzzy distance measures are also investigated. Based on the relationship between distance measure and similarity measure, some novel similarity measures for hesitant fuzzy sets can be further deduced from the proposed distance measures. Finally, two numerical examples are given to demonstrate the applicability and validity of the proposed hesitant fuzzy distance measures.