Small and medium-sized suppliers (SMSs) encounter challenges in reducing carbon emissions (CER) because of financial limitations. Despite this, the potential of two carbon finance models, namely bank-led carbon finance (BLCF) and firm-led carbon finance (FLCF), remains understudied. This study adopts a mixed-methods approach integrating qualitative and quantitative methodologies, combining semi-structured interviews with reinforcement learning simulations to assess the effects of the two financing models on SMSs' CER performance and supply chain economics. The findings indicate that under the FLCF, the core firm promotes consistent CER across SMSs by fulfilling its three supply chain stewardship roles: credit intermediation, service intermediation, and value intermediation. Notably, while BLCF improves aggregate CER levels, it paradoxically increases carbon emissions among high-CER SMSs due to diminishing marginal returns. In contrast, FLCF universally enhances CER efforts, achieving a 50% higher total CER output compared to BLCF. Moreover, FLCF enhances economic performance by 30% for SMSs, 14% for core firms, and 18% for the entire supply chain. The analysis also identifies key moderators, such as bank interest rates and core firms' order allocation preferences, which significantly influence SMSs' CER decision-making processes. These findings highlight the need to prioritize the development of FLCF, which establishes a synergistic ecosystem balancing CER efficacy with long-term economic sustainability.
Recent assessments indicate that even with full implementation of current nationally determined contributions, global temperatures would still rise by 2.5-3 °C by the end of the century (UNEP 2023). This trajectory significantly exceeds the Paris Agreement's 1.5 °C target (Matthews & Wynes 2022), underscoring a persistent gap between climate pledges and actionable mitigation efforts (Bolton & Kacperczyk 2023; Huynh & Xia 2023). Of particular concern is China, which accounts for approximately 32% of global CO emissions, a share that not only continues to grow but also dwarfs the contributions from the U.S. (14%) and the EU (8%). China's commitment to peaking emissions by 2030 and achieving carbon neutrality by 2060 (Zhao et al. 2022) necessitates addressing Scope 3 (value chain) emissions, particularly from small and medium suppliers (SMSs) that account for 65% of China's carbon footprint through these indirect emissions (Meng et al. 2018). However, a significant hurdle for most SMSs in their carbon emission reduction (CER) efforts is a high financing cost (Cecere et al. 2020). A recent survey by the Center for International Knowledge on Development (2023) shows that the average interest rate for China's SMEs is 5.8%, which is nearly 50% higher than the 3.9% rate for large enterprises.
The bank-led carbon finance (BLCF) model has gained prominence in both developed and developing economies as a policy-driven mechanism to reduce financing costs for SMSs undertaking CER initiatives. For example, the European Investment Bank administers the EU Innovation Fund, which allocated €2.87 billion during 2021 and 2023 to finance CER projects by SMSs. A representative example is the €18 million financing extended in 2021 to an Italian cement plant for the CLEANKER carbon capture, utilization, and storage project, achieving a 33% reduction in emissions intensity from 1.2t to 0.8t CO per ton of cement produced. In China, the People's Bank of China (2021) introduced the Carbon Emission Reduction Support Tool, a structural monetary instrument designed to incentivize banks through subsidized refinancing rates to extend CER loans to SMSs. Regulatory disclosures in 2023 revealed that 17 participating banks disbursed $19.42 billion across 1353 CER projects. Notwithstanding these advancements, the BLCF model faces systemic challenges stemming from information asymmetries (Akomea-Frimpong et al. 2022). SMSs' limited capacity for verifiable emissions monitoring results in opacity in CER performance data, constraining banks' ability to conduct comprehensive due diligence (Bertini et al. 2022; Gao & Souza 2022; Beyer et al. 2024). This informational deficit compels financial institutions to incorporate risk premiums into CER financing terms (Huang et al. 2023), disproportionately burdening capital-constrained SMSs.
To address the structural deficiencies of the BLCF model, the firm-led carbon finance (FLCF) model has emerged. This model leverages the resource integration capabilities and information hub role of supply chain core firms to establish vertical collaboration mechanisms (Song et al. 2016; Song et al. 2018), thereby resolving the financing challenges faced by SMSs in implementing CER initiatives (Dou et al. 2015). For example, Walmart requires key suppliers to connect to its Sustainability Hub platform to monitor product lifecycle carbon emissions (Neebe 2020). The platform algorithmically converts emission reductions into tradable carbon credits. Through a partnership with J.P. Morgan, Walmart links suppliers' carbon performance to loan interest rates, with top performers qualifying for preferential rates. Similarly, State Grid Corporation of China, the world's largest utility company, employs an analogous mechanism (Xu et al. 2023). For instance, it requires its suppliers to join the Yingda Carbon Management Platform, which digitally integrates supply chain carbon emission data to generate standardized carbon credits. Additionally, it collaborates with Bank of Communications to develop targeted financial instruments.
However, three critical research gaps persist in the extant literature. First, few studies have empirically validated the BLCF model's long-term efficacy in promoting CER among SMSs, specifically its heterogeneous impacts on firms with varying emission reduction capabilities. Second, how core firms implement their critical functions in the FLCF model remains underexplored. Third, limited data availability constrains cross-model comparisons of BLCF/FLCF mechanisms and the examination of contextual moderators.
To address these research gaps, this study proposes a mixed-methods framework that integrates semi-structured interviews (qualitative) with reinforcement learning modeling (quantitative). The research identifies three critical roles of core firms within carbon finance ecosystems and systematically compares the effectiveness of two carbon finance models in promoting CER among SMSs. Theoretically, this study builds upon supply chain stewardship theory to innovatively propose that core firms perform three intermediary roles (credit, service, and value intermediaries) in the FLCF model. This theoretical advancement clarifies how core firms strategically bridge financial and environmental governance to facilitate supply chain decarbonization. Methodologically, the study pioneers a mixed-methods approach. The research design begins with semi-structured interviews to explore the operational mechanisms of the FLCF model, extracting real-world behavioral features (such as decision motives and incentive mechanisms). These qualitative insights are then operationalized into reinforcement learning parameters (including action spaces and reward functions) to construct computational models. Finally, multiagent simulations are employed to quantitatively validate the decarbonization efficacy of the two finance models. This approach establishes a rigorous, closed-loop validation process that progresses from qualitative theory-building to quantitative verification. By synergizing qualitative depth with computational rigor, this framework not only strengthens the robustness of conclusions but also enhances the generalizability of interview-derived theories through algorithmic replication. The methodology offers a transferable paradigm for interdisciplinary studies on sustainable finance and supply chain governance.
This paper is structured as follows: The "Literature review" section provides a literature review. The "Research methodology" section employs semi-structured interviews to analyze the critical role of core firms and extract real-world behavioral features for the BLCF/FLCF model. The "The semi-structured interviews conducted with the state grid and Yingda" section operationalizes these features into reinforcement learning parameters to develop data-driven computational models. The "Verification experiments and extended analysis based on reinforcement learning" section evaluates the empirical results. The conclusion synthesizes key findings and practical insights.