Green technology innovation is an important means to break out of the constraints of resources and the environment, enhance the competitiveness of enterprises, and achieve the upgrading of industrial structures, and promote high-quality economic growth. In order to realize the overall improvement of the green technology innovation capability of Chinese enterprises, it is necessary to measure the efficiency of industrial enterprises’ green technology innovation and explore their regional differences. In this paper, from the perspective of a two-stage innovation value chain, by introducing the industrial carbon emissions per unit of Gross Domestic Product (GDP) and the “three wastes” pollutants into the research framework of green technology innovation efficiency, we established a novel green innovation efficiency evaluation indicator system for industrial enterprises. Furthermore, we used a two-stage network DEA with shared input to measure the efficiency of regional enterprises’ green technology innovation and explored the regional differences in industrial enterprises’ green technology R&D and the efficiency of green technology achievement transformation. Finally, we provide some suggestions for improving China’s industrial enterprises’ green innovation efficiency, so that they can ameliorate the significant regional imbalances and differences and realize high-quality economic growth.

Quality function deployment (QFD) is a widely used technique for translating customer requirements (CRs) into engineering characteristics (ECs) in product or service design. However, existing improved QFD methods suffer from several limitations, such as relying on precise experts' assessments, subjectivity in aggregating evaluation information, and excessive external information and parameters, which may increase the the complexity of QFD and hinder its practical application. To address these challenges, this article presents a novel rough set theory-based ordinal priority approach (OPA) methodology (OPA-R) to enhance traditional QFD. The proposed approach uses ordinal priorities provided by experts to evaluate CRs and the interrelations between CRs and ECs, eliminating the need for fuzzy linguistic variables, fuzzy numbers, or pairwise comparison matrices. Moreover, rough set theory is employed to aggregate the assessments of experts to generate rough ordinal priorities. An extended optimization model of traditional OPA is then developed to determine the ranking of CRs and ECs. The validity and advantages of the proposed model are demonstrated through a case study in the manufacturing of electric vehicles. The OPA-R method can simplify the process of QFD, reduce the reliance on precise assessments from experts, and avoid excessive external information and additional parameters.

Different from conventional decision-making environments, decision makers (DMs) in a community setting usually exhibit the complex social preferences and intricate social interactions, which may lead to high-decision costs for group consensus reaching. To address this challenge, we design a minimum cost consensus-based social network group decision making (SNGDM) approach considering altruism-fairness preferences and ordered trust propagation. First, a trust propagation method with order effect and path length is proposed to estimate the completed trust relationships among DMs in order to determine the weights of DMs. Then, inspired by the interaction of altruism and fairness preferences, we define the individual altruism-fairness preference utility function and utility level for cost consensus, and explore some properties. Afterwards, a new minimum cost consensus-based SNGDM with individual altruism-fairness preference utility is constructed. Finally, the validity of the proposed consensus framework is confirmed through the carbon reduction consensus problem of China’s aviation enterprises. Moreover, the sensitivity studies and comparative analysis are conducted to further demonstrate the merits of our proposal.