In recent years, the digital economy, with data as the key production factor, has developed rapidly and become the driving force of China’s high-quality economic development. The relationship between data factors and high-quality economic development has gradually become a key topic of academic research. Based on the panel data of 30 provincial-level regions in China from 2014–2021, this study first constructs the proposed data factors and economic high-quality development evaluation indexes, respectively, by using the entropy weight method, and empirically analyzes whether data factors can promote economic high-quality development by using regression models. The results demonstrate the following: (1) Data elements play an important driving role in high-quality economic growth, and they have become an important engine for sustainable economic growth. (2) The development level of data elements has a stable growth rate and obvious regional differences, showing a pattern of “strong in the east and weak in the west, strong in the south and weak in the north”. (3) The degree of influence of data elements on different dimensions of high-quality economic development is inconsistent, with a greater influence on coordinated development, green development, and innovation development, while the effect on open development is not obvious. The above findings provide a reference and scientific basis for countries to formulate relevant policies to fully release the vitality of data elements and promote high-quality economic development.

With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing research, related to low-carbon product family design, did not take into account remanufactured products. Nowadays, it is popular to launch remanufactured products for environmental benefit and meeting customer needs. On the one hand, the design of remanufactured products is influenced by product family design. On the other hand, the launch of remanufactured products may cannibalize the sale of new products. Thus, the design of remanufactured products should be considered together with the product family design for obtaining the maximum profit and reducing the GHG emission as soon as possible. The purpose of this paper is to present an optimization model to concurrently determine product family design, remanufactured products planning and remanufacturing parameters selection with consideration of the customer preference, the total profit of a company and the total GHG emission from production. A genetic algorithm is applied to solve the optimization problem. The proposed method can help decision-makers to simultaneously determine the design of a product family and remanufactured products with a better trade-off between profit and environmental impact. Finally, a case study is performed to demonstrate the effectiveness of the presented approach.

SRDI enterprises are a strong engine for the high-quality development of the economy and an important implementation body for the construction of a strong digital country. With the booming development of digital economy, digital transformation is one of the effective paths to empower the high-quality development of small and medium-sized enterprises. However, the digital transformation of SRDI enterprises is not a simple stacked combination of elements, but rather a complex linkage system that contains many conditional elements. The cracking of the digital transformation of SRDI enterprises urgently needs to be explored by the academic community. In order to analyze the key success factors of digital transformation in SRDI enterprises, to determine whether individual elements constitute the necessary conditions for the digital transformation of an enterprise, and to explore which antecedent configurations are more conducive to the success of an enterprise’s digital transformation, this study takes 39 “SRDI” enterprises in Zhejiang Province as samples and uses the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method. Based on the “technology-organization-environment” framework, it examines the digital transformation path of “SRDI” enterprises from a configurational perspective. The following results were found in this study: the group patterns leading to high levels of digital transformation can be divided into the following four types: organization-environment linkage, pressure-strategy synergy, organization-led, and total-factor-driven. The group patterns leading to low levels of digital transformation can be divided into the following four types: total-factor-deficient, single-technology type, technology-biased, and organization-deficient. A digital strategy is an important condition for SRDI enterprises to realize high-level digital transformation, and the external environmental influences contribute to the realization of digital transformation and upgrading of enterprises, with the pressure of industry competition being more critical.

Product family (PF) design is a widely used strategy in the industry, as it allows meeting diverse design requirements. Change propagation in any PF is difficult to predict. Consequently, while numerous design change management methodologies presently exist, their application is restricted to a single artifact. This issue is overcome in the present study. The proposed framework explores effective change propagation paths (CPPs) by considering the risks associated with design changes in the PF with the aim of minimizing the overall redesign cost. The propagated risk, which would result in rework, is quantified in terms of change impact and propagation likelihood. Moreover, a design structure matrix (DSM) based mathematical model and an algorithm for its implementation are proposed to investigate the change propagation across the PF. Finally, to demonstrate their effectiveness, a PF of electric kettles is examined in a case study. The study findings confirm that the proposed technique is appropriate for evaluating different CPPs in PF.

With the increase in pollution and people’s awareness of the environment, reducing greenhouse gas (GHG) emissions from products has attracted more and more attention. Companies and researchers are seeking appropriate methods to reduce the GHG emissions of products. Currently, product family design is widely used for meeting the diverse needs of customers. In order to reduce the GHG emission of products, some methods for low-carbon product family design have been presented in recent years. However, in the existing research, the related GHG emission data of a product family are given as crisp values, which cannot assess GHG emissions accurately. In addition, the procurement planning of components has not been fully concerned, and the supplier selection has only been considered. To this end, in this study, a concurrence optimization model was developed for the low-carbon product family design and the procurement plan of components under uncertainty. In the model, the relevant GHG emissions were considered as the uncertain number rather than the crisp value, and the uncertain GHG emissions model of the product family was established. Meanwhile, the order allocation of the supplier was considered as the decision variable in the model. To solve the uncertain optimization problem, a genetic algorithm was developed. Finally, a case study was performed to illustrate the effectiveness of the proposed approach. The results showed that the proposed model can help decision-makers to simultaneously determine the configuration of product variants, the procurement strategy of components, and the price strategies of product variants based on the objective of maximizing profit and minimizing GHG emission under uncertainty. Moreover, the concurrent optimization of low-carbon product family design and order allocation can bring the company greater profit and lower GHG emissions than just considering supplier selection in low-carbon product family design.