In the current context of severe environmental challenges and climate change, the low-carbon green development model has become an international consensus. This study establishes a green supply chain duopoly competition model, considering two types of government subsidies and data-driven marketing (DDM) to help achieve supply chain development. The aim of the research is to explore how to provide green subsidies, enhance green levels, maintain competitive advantage, and improve profits in supply chain enterprises with inconsistent green levels. The study discusses the impact of green consumer preferences, market competition, and DDM quality on the profits of supply chain enterprises. It also analyzes how to use supply chain contracts to achieve coordination and optimization within the supply chain. The findings are summarized as follows. (1) As consumer preferences for green products increase, the unit subsidy model continues to enhance performance and market share more effectively than the total subsidy model. (2) The unit subsidy model requires a more relaxed subsidy coefficient, making it easier for enterprises to develop without needing high subsidies. It consistently achieves better total performance, particularly with improved DDM quality. (3) Manufacturers and retailers can achieve a win–win situation through internal coordination of the supply chain via wholesale price contracts. (4) Under certain conditions, consumers more sensitive to green products will increase the product pricing of both M1 and M2. The level of greenness of M2 will also increase. But also, the wholesale and retail prices of M1 will decrease because of the effect of DDM. (5) The effect of the intensity of market competition on pricing decisions is more complex. Under certain conditions, the market competition coefficient has a positive impact on the pricing of M1 and a negative impact on the pricing and green level of M2. This can be changed due to an increase in the level of DDM quality, where an increase in the market competition coefficient results in lower pricing for M1 and higher pricing for M2. The green level for M2 is also improved. In addition, the improvement in DDM quality consistently has a positive impact on pricing decisions and green levels for M2. Pricing decisions for M1 are affected differently, depending on the customer’s sensitivity to DDM.

The rise of the circular economy and e-commerce has led to the emergence of e-commerce closed-loop supply chains (ECLSCs). In practice, investing in process innovation (PI) is key to improving profitability and competitiveness. However, manufacturers at the downstream of ECLSCs often face financial constraints and quality uncertainty of used products, while research on how to select financing strategies under these conditions remains limited. To explore the optimal financing scheme for the ECLSC, this study investigates two financing schemes: bank financing (BF) and FinTech platform financing (FPF), which offers a combination of debt financing (DF) and equity financing (EF). Some key findings are derived. For the ECLSC, the FPF scheme is more profitable when the unit manufacturing cost for new components exceeds the threshold or PI costs are relatively low. Additionally, the FPF performs better when the FPF interest rate is low and the DF ratio is high. The BF is more beneficial when consumer sensitivity to recycling prices or service is low. The FPF enables the ECLSC to achieve maximum profits and minimize environmental impact within a specific range. Furthermore, the financing models are extended to incorporate considerations of fairness, where the optimal financing scheme is primarily influenced by the manufacturing cost.

Purpose: Manufacturing firms increasingly invest in artificial intelligence (AI) to support digital transformation and industrial decarbonization, yet these investments do not consistently generate meaningful environmental improvements. Addressing this sustainable development problem, this study aims to examine how supplier and customer integration shape the relationship between AI capability and environmental performance. Design/methodology/approach: Grounded in dynamic capabilities theory (DCT), this study tests a moderated configurational model using survey data from 426 Chinese manufacturing firms. Hierarchical regression and three-way interaction analysis examine the moderating and synergistic roles of supplier and customer integration. Robustness checks address non-response bias, common method bias and endogeneity through 2SLS. Findings: AI capability is positively associated with environmental performance, and supplier integration strengthens this relationship. Customer integration has no stable standalone moderating effect, but its joint alignment with supplier integration produces a significant positive three-way interaction. AI therefore generates greater environmental value when embedded in a bidirectionally integrated supply chain. Practical implications: The findings suggest that firms should not deploy AI systems in isolation but align them with upstream and downstream integration mechanisms. Strengthening supplier collaboration enhances structured data inputs for AI optimization, while coordinated customer integration helps align environmental innovation with market expectations. Managers should embed AI initiatives within closed-loop supply chain governance structures to maximize sustainability outcomes. Social implications: The societal value of AI-enabled manufacturing depends on supply-chain arrangements that support reliable environmental information and coordinated action. Policies integrating digital upgrading with environmental data standards, responsible procurement and cross-organizational accountability can better advance sustainable industrialization, responsible production and climate action, thereby contributing to SDGs 9, 12 and 13. Originality/value: This study conceptualizes AI capability as an analytical enabler of dynamic capabilities rather than a standalone technological resource. It refines the application of DCT by introducing a technology–structure synergy perspective, revealing the configurational and threshold conditions under which AI translates into environmental performance. By empirically identifying a three-way interaction effect, this study bridges digital capability research and sustainable operations management within a supply chain embeddedness framework.

The rise of the circular economy and e-commerce has led to the emergence of e-commerce closed-loop supply chains (ECLSC). In practice, investing in process innovation (PI) is key to improving profitability and competitiveness. However, manufacturers at the down-stream of ECLSC often face financial constraints and quality uncertainty of used products, while research on how to select financing strategies under these conditions remains lim-ited. To explore the optimal financing scheme for the ECLSC, this study investigates two financing schemes: bank financing (BF) and FinTech platform financing (FPF), which combines debt financing (DF) and equity financing (EF). Some key findings are derived. For the ECLSC, the FPF scheme is more profitable when the unit manufacturing cost for new components exceeds the threshold or PI costs are relatively low. Additionally, the FPF performs better when consumer sensitivity to recycling prices is high. The BF is more ben-eficial when the FPF scheme's interest rate and DF ratio are relatively high. The FPF ena-bles the ECLSC to achieve maximum profits and minimize environmental impact within a specific range. Furthermore, the financing models are extended to incorporate consider-ations of fairness, in which manufacturing costs primarily influence the optimal financ-ing scheme.

Purpose: This study investigates how and when digital twins (DTs) adoption translates into operational performance. Design/methodology/approach: Using survey data from 417 Chinese manufacturing firms, mediation and moderated mediation analyses were conducted to examine agile manufacturing as a capability mechanism and supply chain transparency as a boundary condition. Findings: The results show that DTs adoption improves operational performance through agile manufacturing. However, this capability pathway becomes weaker as supply chain transparency increases. Originality/value: By integrating dynamic capabilities theory and information processing theory, this study reconceptualizes DTs adoption as a dynamic organizational capability and reveals the contingent nature of DTs-enabled value creation under varying levels of supply chain transparency.