Study on phased strategies for sustainable aviation fuel (SAF) industrialization based on a tripartite evolutionary game

Against the “high-carbon lock-in” dilemma in sustainable aviation fuel (SAF) industrialization, which is caused by high costs, insufficient supply, and a lack of policy coordination, this study constructs a tripartite evolutionary game model involving governments, airlines, and fuel producers to examine industrialization across initial, developmental, and mature stages. We establish dynamic replication equations, solve Jacobian matrices, and conduct numerical simulations to reveal evolutionary paths and equilibrium conditions of strategic interactions among the three parties at different stages. The results show that a high-intensity policy mix of over 60% government subsidies for airlines SAF purchase premiums plus a high carbon tax is required initially to break the deadlock; in the development stage, airlines need to cover at least 33.3% of fuel producers R&D costs and offer no less than 15% profit share, while carbon tax intensity should increase by more than 60%. The results show that in the initial stage, a high-intensity policy combination of “government subsidies covering more than 60% of airlines’ SAF purchase premium + high carbon tax” must be implemented to break the “high-carbon lock-in” deadlock during the introduction period of industrialization. In the development stage, to address the bottleneck of “weak supply-demand coordination”, airlines need to bear no less than 33.3% of fuel producers’ R&D costs through investment and provide a profit share of no less than 15%. Moreover, the intensity of the carbon tax should be increased to more than 1.6 times the original level. In the mature stage, the carbon quota price for airlines should be set such that the selling price is at least 0.75 times the purchase cost. Under this premise, the SAF will gradually gain cost competitiveness and economies of scale. This study further proposes building a dual-drive mechanism integrating green finance, offtake agreements and industrial chain equity investment, and suggests government shift from “dominant supervision” to “service support”, and solve the problem of “lack of market mechanisms” by improving the carbon market and emission evaluation system, ultimately promoting the SAF to achieve a fundamental transformation from “policy dependence” to “market autonomy”.

1 Introduction

Although the air transport industry accounts for only 2%–3% of global carbon emissions (Graver et al., 2020), amid the global drive toward carbon peaking and carbon neutrality, its high unit energy consumption and concentrated high-altitude emissions make its environmental impact particularly pronounced. Against the backdrop of the global aviation industry’s goal to achieve neutral aviation carbon emissions by 2050, the civil aviation sector has developed relatively clear alternative pathways in key technological fields, such as sustainable aviation fuel (SAF), electric aircraft, and hydrogen propulsion, while demonstrating potential for large-scale application. Consequently, a series of international climate policies have designated the aviation industry as a priority pilot sector, actively promoting its low-carbon transition through institutional innovation and policy experimentation (Dray et al., 2021; Winchester et al., 2015). The International Air Transport Association (IATA) reported that the SAF must account for 65% of emission reduction efforts (International Air Transport Association, 2021), and the International Civil Aviation Organization (ICAO) also recognized the SAF as the optimal practical solution for achieving carbon neutrality in civil aviation. SAF is a green alternative energy source derived from nonpetroleum feedstuff, which can be blended with traditional aviation kerosene for use. International organizations and governments worldwide have continuously increased support for the SAF industry and have introduced a range of policies to reduce aviation emissions. For instance, ICAO adopted the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) in 2016, explicitly designating the SAF as a core emission reduction measure to promote global research and development (R&D), production, and industrialization processes. In 2021, the European Union (EU) launched the “Fit for 55”package, mandating that the blending ratio of SAFs in aviation fuel will reach 2% by 2025 and increase to 5% by 2030. On 4 October 2021, the 77th Annual General Meeting of IATA approved a resolution for the global air transport industry to achieve net-zero carbon emissions by 2050. According to the milestone targets outlined in this resolution, global SAF consumption will reach 18 million tons (23 billion liters, accounting for 5.2% of total fuel demand) by 2030 and 350 million tons (449 billion liters, accounting for 65.0% of total fuel demand) by 2050 (Zhang, 2024). Aligned with CORSIA, the EU requires that starting from 1 January 2025, all aviation fuel supplied at EU airports must contain at least 2% SAF blended with traditional kerosene; this ratio will increase to 6% by 2030 and 70% by 2050. Driven by these policies, more than 450,000 flights worldwide have used SAF-blended fuel, and more than 50 airlines have gained experience in SAF application—indicating that the SAF is transitioning from demonstration to industrial application. The term “SAF industrialization” refers to a systematic process that advances the SAF from experimental R&D and demonstration applications to full commercial supply through technological breakthroughs, large-scale production, market mechanisms, and policy coordination. Its core lies in establishing a full industrial chain system covering feedstock supply, refining and processing, fuel certification, airport refueling, and the feedback of emission reduction benefits. Its primary goal is to achieve carbon neutrality in the aviation industry through large-scale production technologies while balancing economic feasibility and environmental sustainability.

As a major civil aviation country, China initially developed the world’s largest and most comprehensive carbon emission reduction policy system after it proposed the goals of carbon peaking and carbon neutrality in 2020. The Communist Party of China (CPC) Central Committee and the State Council have successively issued “Opinions on Fully, Accurately and Comprehensively Implementing the New Development Concept to Advance Carbon Peaking and Carbon Neutrality” (The Communist Party of China Central Committee and the State Council, 2021) and “Carbon Peaking Action Plan Before 2030” (The State Council, 2021), establishing the top-level design framework of the “1 + N” policy system. They have also set specific targets, such as “by 2030, the share of nonfossil energy consumption will reach approximately 25%, and carbon dioxide emissions per unit of GDP will decrease by more than 65% compared with 2005”. As a key sector for carbon emissions, civil aviation was included in the “Ten Carbon Peaking Actions” in the “Carbon Peaking Action Plan Before 2030”, which explicitly requires “promoting the low-carbon transformation of civil aviation equipment and building a green and efficient air transport system”.

1.1 The dilemma of SAF industrialization development

However, China’s SAF industrialization involves a sharp contradiction between “policy goals and practical capabilities”. Unlike international progress in terms of SAF industrialization, China’s SAF industry remains in its initial stage. In 2023, China’s SAF production capacity accounted for less than 10% of the global total, with 90% relying on imports. In 2022, the Civil Aviation Administration of China (CAAC) released “the 14th Five-Year Special Plan for the Green Development of Civil Aviation” (Civil Aviation Administration of China, 2022), setting a 2025 SAF consumption target of 20,000 tons—a significant gap compared with that of European countries and the United States, reflecting insufficient full-chain industrialization capabilities. To advance SAF development, China’s “14th Five-Year Plan for the Modern Energy System” (National Development and Reform Commission and National Energy Administration, 2022) explicitly proposed promoting the application of advanced biofuels in the aviation sector to develop a green and low-carbon aviation energy system. In 2023, four ministries, including the Ministry of Industry and Information Technology, jointly issued “the Outline for the Development of Green Aviation Manufacturing Industry (2023–2035)” (Ministry of Industry and Information Technology et al., 2023), further proposing that” by 2025, demonstration applications of domestic civil aircraft using sustainable aviation fuel will be realized”. Research by Liu et al. (2025) revealed that China’s SAF industry faces challenges such as insufficient technological reserves, high prices, limited production capacity, and aromatic content restrictions, which hinder the large-scale application of SAF. China’s SAF industrialization is clearly confronted with dilemmas, including “weak supply, high costs, and insufficient infrastructure”. Existing policies mostly remain at the level of “unified blending ratios and general subsidies” and fail to address stage-specific core pain points: the 3–5 times cost premium in the initial stage of SAF industrialization, the supply–demand mismatch in the development stage where “airlines are reluctant to purchase and producers are reluctant to expand production”, and the lack of a linkage mechanism between carbon allowances and SAF emission reduction benefits in the mature stage. Such policy inadequacies will impede the achievement of China’s civil aviation “dual carbon goals”. Currently, the industrialization of the SAF still faces numerous challenges. According to statistics from the International Energy Agency (IEA), the global SAF production capacity in 2023 was only 50 million liters, accounting for less than 0.1% of the total aviation fuel demand (International Energy Agency, 2023a); its production cost is 3.1 times that of traditional aviation fuel (International Energy Agency, 2023b); the raw material supply gap is as high as 85% (Smith et al., 2023); and the coverage rate of airport blending and refueling facilities is merely 11.8% (International Air Transport Association, 2023). These figures highlight the three dilemmas of “unaffordability”, “insufficient supply”, and “coordination difficulties”. It can be seen from the research on SAF industrialization needs to be further enhanced. For example, there are relatively few studies on raw material supply, production technology, and industrial chain supporting facilities in the SAF industry. How to promote the development of SAF industry from a systematic perspective will be related to the sustainable development of the civil aviation industry.

1.2 SAF industrialization policy coordination

Production enterprises face significant investment risks because of high costs, long certification cycles, and strict requirements for raw material sustainability; airlines lack motivation to adopt SAF because of high price premiums and the failure to internalize emission reduction benefits; and government supervision lacks dynamic coordination, and tools such as carbon taxes and subsidies fail to form a synergy, exacerbating “policy failure” (Li et al., 2022; Wei and Liao, 2021). To address these issues, the academic community has proposed various solutions from a technoeconomic perspective, such as carbon tax thresholds, R&D subsidies, and policy tool combinations. Schäfer et al. (2019) utilized marginal abatement cost curves to demonstrate that a carbon tax of $200 per ton of CO₂ is necessary to incentivize airlines to adopt an SAF on a large scale; Liu and Sheng’s (2023) supply chain simulations revealed that carbon tax design is nuanced—a rate that is too low fails to provide sufficient incentives, whereas one that is too high suppresses technological innovation, whereas a moderate rate can increase the profits of clean-tech manufacturers by 12%–18%. He et al. (2022) compared different subsidy methods and reported that R&D subsidies can increase manufacturers’ emission reduction investments by 30%, which is more conducive to technological innovation; Cui et al. (2024) reported that in scenarios with low traditional fuel prices and strict emission regulations, subsidy policies are more beneficial to social welfare than quotas. Scholars’ research mainly focuses on explaining the relationship between SAF industry development and policies, but neglects the policy adaptation research in different development stages of SAF industrialization. For example, the initial stage mainly focuses on breaking through the high production cost of SAF, the development stage focuses on solving the supply and demand mismatch, and the mature stage focuses on coordinating market vacancies. Furthermore, most focus on single policy tools or static analysis, failing to systematically depict the strategic interaction and feedback mechanisms among the “government-airline-producers” triad; they lack dynamic research on the adaptability of policy combinations at different stages of the industrial life cycle, and they ignore the in-depth impact of market-oriented mechanisms on the strategic choices of actors. These limitations have led to a long-term “one-size-fits-all” approach in SAF industrialization policies—lack of high-intensity breakthrough tools in the initial stage, neglect of industrial chain coordination mechanisms in the development stage, and failure to withdraw policy intervention in a timely manner in the mature stage. There is an urgent need to construct a “phased and dynamic” tripartite interaction model.

1.3 SAF industrialization multi-agent game

In recent years, evolutionary game theory has been widely used in the study of SAF Industrialization and multi-actor behavior decisions in aviation emission reduction because of its ability to handle strategic interactions and dynamic evolution under bounded rationality. Guo et al. (2023) reported through an evolutionary game model that government subsidies are more sensitive to manufacturers, while carbon taxes are more effective for suppliers; Zhang et al. (2025) reported that the combination of carbon trading and subsidies can more effectively guide the system toward low-carbon equilibrium. Wang et al. (2022) and Liu et al. (2023), among others, studied the effectiveness of carbon allowance allocation and carbon market mechanisms, respectively; Chen et al. (2022) explored multiparty games under the international route carbon offset mechanism (European Commission, 2023). However, these studies mostly focus on macro carbon reduction strategies or carbon market mechanisms, fail to conduct in-depth analysis by taking SAF as an independent decision variable, and lack targeted modeling for its industrialization dilemmas such as high cost, mismatch between supply and demand, and difficulty in supply chain coordination. As shown in Table 1 below.

Table 1

Table 1. Corresponding relationship between literature context and scientific questions.

In summary, existing studies either focus on unilateral policies or are limited to bilateral games, and most rely on static analysis. They lack a dynamic tripartite model that treats Sustainable Aviation Fuel (SAF) as an independent decision-making variable, nor have they internalized industrial life cycle stages into transferable equilibrium conditions. For the first time, this study integrates the aforementioned fragmented studies into a unified framework of “mechanism design—dynamic evolution—inter-stage transition,” addressing the gaps shown in Table 1. This paper addresses the market failure phenomenon of “high cost-low supply-weak coordination” and proposes a core scientific question from the perspective of mechanism design. Under the conditions of information asymmetry and externality, how to drive the strategy evolution of the three main players from ‘high-carbon lock-in’ to ‘market-oriented equilibrium’ through phased policy and market mechanism design? To this end, a tripartite evolutionary game model of government-airline-fuel producer is constructed to systematically study the phased strategies for promoting the industrialization of SAF.

The innovative contributions of this study can be summarized into three points: Firstly, Perspective of the research question: For the first time, it proposes the scientific question of the lack of dynamic market mechanisms in Sustainable Aviation Fuel (SAF) industrialization from the perspective of mechanism design. Secondly, Modeling method: It constructs a tripartite evolutionary game with three-dimensional matching of “stage-subject-tool”, and internalizes the life cycle characteristics into transferable equilibrium conditions. Finally, Policy output: It presents the threshold combination for inter-stage transition (subsidy ≥66.7%, carbon tax intensity ≥1.6 times, profit share ≥15%, carbon quota ≥0.75 times), which can be directly embedded into the policy dashboards of various countries.

Therefore, on the basis of existing theoretical frameworks, this study focuses on the full life cycle of SAF industrialization and constructs a tripartite evolutionary game model involving the government, airlines, and producers. The objectives are to systematically reveal the dynamic evolutionary paths and policy needs in SAF promotion. First, to construct a tripartite evolutionary game model to clarify the dynamic feedback mechanism between policy tools and actor behaviors. Second, to internalize the full life cycle characteristics of the SAF industry into the model parameters, realizing direct mapping between industrial development stages and policy intervention strategies. Finally, to quantify the synergy effects and key thresholds of different policy combinations through numerical simulations and multiscenario sensitivity analysis, and propose a “phased, differentiated, and dynamically adjusted” policy framework. This study provides a reference for governments to design precise incentive and supervision strategies to promote SAF industrialization.

2 Construction of the evolutionary game model

On the basis of the three major structural bottlenecks and their internal contradictions faced by SAF industrialization, namely, cost dilemmas, supply gaps, and policy coordination failures, this paper aims to construct a theoretical framework for systematically quantifying the dynamic interaction of multiple subjects. Although existing studies have proposed a phased policy approach, there is a lack of systematic research on the strategic interaction among the “government-airline-producer” tripartite. To overcome the current deadlock of “high-carbon lock-in” in civil aviation, this paper introduces evolutionary game theory and constructs an analytical framework that conforms to the multistage characteristics of SAF industrialization.

The tripartite evolutionary game is established under an institutional setting in which (i) the government possesses regulatory authority to design and enforce carbon-related instruments (e.g., carbon taxes, quota/credit mechanisms, compliance penalties) and (ii) the government has fiscal and administrative capacity to implement targeted subsidies and monitoring. In this paper, the government’s strategic choice (“Supervision” vs. “Non-supervision”) is interpreted as an endogenous entry/exit decision under bounded rationality: the government tends to enter active supervision when the expected governance payoff (e.g., environmental benefits, fiscal/credibility gains, and policy performance returns represented by the payoff term W and related items) outweighs the sum of regulatory costs and subsidy expenses (e.g., Cg and subsidy outlays), and when the expected environmental governance burden under non-supervision (e.g., E4 + E5) is sufficiently large. This interpretation is consistent with the payoff structure defined in Tables 1, 2 and will be explicitly tested through the ESS stability inequalities reported in Table 4.

Table 2

Table 2. Specific game parameters and their significance.

In addition, because existing SAF support policies are typically pilot-based and stage-dependent, this study does not assume a permanently “high” subsidy regime; instead, the subsidy parameter is treated as a scenario lever representing heterogeneous policy willingness and fiscal constraints across industrialization stages, which is further examined in the sensitivity analysis.

2.1 Logical relationship of the evolutionary game model

The government, fuel producers, and airlines form a mutually dependent and dynamically evolving strategic interaction relationship, which is reflected specifically in the following three aspects.

2.1.1 Government and fuel producers: policy levers driving technological investment

Governments intervene in the decision-making processes of producers via strategic instruments such as subsidies and carbon taxes. Taking the EU’s “ReFuelEU” Regulation as an example, this policy mandates that airlines gradually increase the SAF blending ratio (to 2% by 2025), levies high carbon taxes on noncompliant enterprises, and offers subsidies to compliant enterprises (Parry et al., 1999) to drive capacity expansion. In the carbon tax model constructed by Parry (Neste Corporation, 2023) and others, the government restricts the carbon emissions of fuel producers by levying a carbon tax on each ton of CO₂ generated during the production of traditional aviation fuel (TAF). The tax rate is multiplied by the emission volume to drive emission reduction. By pricing the social and environmental damage caused by carbon emissions and internalizing them into the decisions of producers and consumers, the goal of emission reduction is achieved efficiently through market mechanisms. Since December 2024, China has abolished the export tax rebate for waste oils and fats, prompting more waste cooking oil to be diverted to domestic SAF production. Fuel producers have also made full use of carbon emission reduction policies to promote the industrialization of the SAF. For example, Finland’s Neste successfully launched a 500,000-ton annual SAF project with government funding (Sasol and Talos Energy, 2023). In March 2024, Sinopec and Total Energies signed a cooperation framework agreement in Beijing to jointly produce an SAF by using waste oils at one of Sinopec’s refineries, with an annual production capacity of 230,000 tons; these measures have significantly reduced the risks and uncertainties of SAF production (Fortune Golden Key, 2024). This finding shows that through a combination of “incentive-constraint” policies, the government can effectively promote fuel producers to invest in green technology and transform production capacity, provide a core driving force for the initial stage of SAF industrialization, and lay an institutional foundation for “supply-demand cooperation” between producers and airlines.

2.1.2 Fuel producers and airlines: economic closed-loop ensuring sustainable supply

Producers and airlines establish stable business cooperative relationships through long-term supply agreements and profit-sharing mechanisms. For example, Sasol and Talos Energy in the United States rely on tax credits provided by the Inflation Reduction Act to jointly promote green, hydrogen-based SAF projects (Sasol and Talos Energy, 2023), and Delta Air Lines signed a one billion-gallon SAF procurement agreement and used 20% of its profits to support R&D investment on the production side, realizing cost sharing and supply stability (Delta Air Lines, 2022). At present, the total production capacity of SAF projects completed, in operation, and planned in China has exceeded 5 million tons per year, with dozens of participating enterprises (Yang, 2025). However, the application of the SAF in China’s domestic airlines is not widespread.

For example, China Eastern Airlines used 537.27 tons of SAF in 2024 and signed a SAF procurement agreement with China National Aviation Fuel Group. In October 2024, Juneyao Airlines used the SAF for Shanghai-Shenzhen flights and operated the first “carbon-neutral” themed flight among private airlines. Through comparison, foreign airlines provide references for China’s airlines in the use of the SAF. By making long-term procurement commitments to lock in the SAF supply and then supporting producers’ continuous R&D through profit sharing, airlines can effectively solve the dual dilemmas of “suppliers daring not to expand production and demanders daring not to purchase” in the process of industrial development. The “off-take agreement + profit sharing” mechanism builds a business closed loop between supply and demand, which not only stabilizes producers’ expectations of capacity investment but also reduces the procurement cost risks of airlines, becoming a key mechanism to promote the large-scale expansion of the SAF.

Airlines are heterogeneous in their financial capacity, risk tolerance, network structure, and strategic incentives; therefore, their willingness and ability to participate in SAF industrialization (e.g., cost-sharing of producers’ R&D, long-term offtake commitments, or profit-sharing arrangements) is not uniform. In practice, upstream participation is more feasible for large full-service carriers and hub/network airlines with stronger balance sheets, stable cash flows, and longer planning horizons, as well as for airlines facing stronger policy mandates or reputational incentives. By contrast, small carriers and many low-cost carriers are typically more cost-sensitive and liquidity constrained, making direct R&D cost-sharing and profit-sharing arrangements less likely. Accordingly, the “airlines’ participation” mechanism in our model should be interpreted as representing a subset of airlines with sufficient capability and incentive to engage in upstream risk-sharing, rather than a universal assumption applying to all airline types.

2.1.3 Government and airlines: carbon trading mechanism coordinating carbon emission reduction

The aviation industry is included in the carbon trading mechanism, and each airline will be allocated initial carbon quotas and manage emissions by participating in carbon trading. If an airline fails to use up the quotas within the specified period, it can choose to sell the remaining carbon quotas; in contrast, if it exceeds the quota limit, it needs to purchase additional carbon quotas to offset emissions. The government allocates or auctions carbon quotas through the Emissions Trading System (ETS) to encourage airlines to actively adopt the SAF to achieve emission reduction goals. Cathay Pacific Airways participates in the Hong Kong carbon trading market and offset flight emissions by purchasing quotas issued by the government. According to the agreement reached in 2023, Cathay Pacific Airways needs to pay carbon taxes if it fails to meet the standards and can receive a tax refund from the government if it meets the standards (Hong Kong Environmental Protection Department, 2023); moreover, the company also invests in producer Gevo to lock in long-term SAF supply (Cathay Pacific, 2022), forming a closed-loop linkage driven by both “regulation and market”. This finding shows that the carbon market mechanism can internalize the emission costs of airlines, encourage them to actively choose low-carbon paths, and form strategic coordination with the production side. Carbon quota trading not only forces airlines to actively participate in SAF supply chain cooperation but also provides a market basis for the government to flexibly adjust the intensity of supervision, ultimately promoting the formation of a closed-loop industrialization with a tripartite coordination of “policy-market-enterprise”. For example, in September 2024, the National Development and Reform Commission and the Civil Aviation Administration of China jointly launched a pilot project for SAF application. The pilot project is carried out in phases. In the initial stage, major airlines such as Air China, China Eastern Airlines, and China Southern Airlines will refuel with SAF on 12 routes at four airports, including Beijing and Chengdu, and the scope is planned to expand in 2025.

In summary, the government encourages fuel producers to carry out green transformation through policy tools, producers establish stable supply relationships with airlines through commercial agreements, and airlines form emission reduction linkages with the government through the carbon market mechanism. Together, the three parties form a closed-loop system, emphasizing the coordination of policy-driven, production response, and consumption-driven mechanisms to promote the sustainable development of SAF industrialization. The logical relationships among the three parties of the “government-airline-fuel producer” evolutionary game constructed in this paper are shown in Figure 1.

Figure 1

Figure 1. Logic diagram of the three-party evolutionary game model.

From a participant perspective, (i) the government faces a trade-off between governance benefits (emission reductions, policy credibility, industrial upgrading) and the fiscal/administrative burden of subsidies and supervision; (ii) airlines balance compliance pressure and brand/market benefits against the incremental fuel cost premium and the risk of upstream investment; and (iii) fuel producers trade off high R&D/scale-up costs and technology uncertainty against expected policy incentives, off-take security, and long-term profitability. These asymmetric objectives and constraints motivate boundedly rational adjustment of strategies and justify the evolutionary-game setting adopted in this paper.

2.2 Basic assumptions of the evolutionary game model

2.2.1 Game subjects

The game involves airlines (Player I), government supervision departments (Player II), and fuel producers (Player III). All three are bounded rational participants, and their strategic decision-making process gradually develops over time and stabilizes at the optimal decision.

2.2.2 Probability of behavior strategy selection

It is assumed that in the initial stage of the game among the three groups of airlines, government supervision departments, and fuel producers, the proportion of airlines choosing the “active response” strategy (purchasing the SAF and blending it with the TAF for use) is $x$ , and the proportion choosing the “passive response” strategy (purchasing only traditional aviation fuel) is $1-x$; the proportion of government supervision departments choosing the “supervision” strategy (implementing subsidies + carbon taxes + supervision) is $y$, and the proportion choosing the “nonsupervision” strategy is $1-y$; the proportion of fuel producers choosing the “active production” strategy (SAF R&D and capacity building) is $z$ , and the probability of choosing the “passive production” strategy (producing only traditional aviation fuel) is $1-z$ ; $x,y,z\in \left[0,1\right]$ among them.

In the present model, “Supervision” encompasses the government’s active entry into SAF promotion through a combination of subsidies, compliance monitoring, and carbon-related regulatory instruments, whereas “Non-supervision” represents a low-intervention stance. Importantly, this is not an exogenous assumption: the likelihood of adopting “Supervision” increases when the expected net payoff of intervention is positive, i.e., when governance gains (including environmental benefits and policy-performance returns) exceed regulatory costs and subsidy expenditures, and when the expected environmental governance burden under non-supervision is high. This endogenous entry logic is embedded in the payoff matrix and reflected in the replicator dynamics.

Moreover, given that subsidy programs for SAF are typically constrained by fiscal capacity and may be implemented as pilots or stage-wise packages, we treat the subsidy intensity as a stage-varying policy lever rather than assuming a uniform, always-on subsidy regime. This treatment enables the model to capture heterogeneous government willingness and policy feasibility across early, development, and mature stages.

2.2.3 Variables related to game behavior

The relevant variables are set according to the logical relationship of the evolutionary game model to ensure that they conform to the actual industrial background and have theoretical support. Zhang et al. constructed an evolutionary game model of large and small airlines and the government, analyzed the synergy mechanism of carbon taxes and subsidies, especially the setting of the sensitivity threshold of subsidies to airlines’ procurement willingness, and verified that the mixed mechanism can promote airlines to choose low-carbon strategies (Zhang et al., 2023); in this paper, the synergy mechanism of carbon taxes and subsidies, especially the setting of the sensitivity threshold of subsidies to airlines’ procurement willingness, is used to set the value range of key policy variables such as government subsidies and carbon taxes. By drawing on the principles of carbon tax rate setting by Parry et al., that is, the carbon tax should cover the environmental external costs in the production process of traditional aviation fuel, it is applied to the benchmark setting and phased adjustment logic of producers’ carbon tax in the model. Karanki & Yu’s research evaluated the bargaining power of airports in the fuel supply chain through the stochastic frontier analysis method (Karanki and Yu, 2025).

This paper draws on its supply chain coordination and investment return mechanism, especially the equity investment and profit-sharing model of airlines to producers, to set variables such as airlines’ investment costs and profit-sharing ratios in the model, reflecting the role of industrial chain coordination in promoting SAF industrialization. As shown in Table 2.

2.2.4 Assumptions on the stage dynamics of strategy selection

The strategic choices of the three parties are dynamically adjusted with the stages of SAF industrialization—in the initial stage, the subjects’ strategies are dominated by “cost constraints”, in the development stage by “synergy benefits”, and in the mature stage by “market benefits”, which conforms to the evolution law of industrialization from “policy dependence” to “market autonomy”.

2.2.5 Institutional preconditions and entry conditions of government regulation

This tripartite evolutionary game is established under the institutional setting where the government has (i) the authority to implement carbon-related regulatory instruments (e.g., carbon tax, quota trading rules) and (ii) the fiscal and administrative capacity to provide targeted subsidies and monitoring. In the model, the government’s strategic choice (“Supervision” vs. “Non-supervision”) reflects an endogenous entry/exit behavior driven by net payoffs under bounded rationality. Specifically, the government is more likely to choose “Supervision” when the expected regulatory payoff (including fiscal revenues and governance benefits, denoted by W and related terms) exceeds the sum of regulatory costs and subsidy expenditures (e.g., Cg and subsidy outlays), and when the expected external environmental governance burden under non-supervision (e.g., E4+E5) is sufficiently high. This mechanism is consistent with the payoff structure defined in Tables 1, 2 and is explicitly tested in the stability conditions in Table 4.

In practice, China’s SAF policy instruments are currently characterized by stage-dependent pilot programs and limited but growing support measures (e.g., demonstration applications, R&D support, and regulatory pilots). Therefore, in this study, subsidy intensity is not assumed to be permanently “high”, but is treated as a stage-varying policy lever consistent with the industrial life-cycle logic (initial–development–mature). We further clarify the ranges and rationale of subsidy-related parameters in Section 4.1 and provide a sensitivity analysis to reflect the government’s heterogeneous willingness and fiscal constraints across stages.

2.3 Establishment of the tripartite evolutionary game model

On the basis of the binary strategy choices of the three subjects (airlines, government supervision departments, and fuel producers) (airlines: active/passive, government: supervision/nonsupervision, producers: active/passive), according to the “2 × 2 × 2” combination logic, a total of 8 strategy combinations are formed, covering all possible strategic scenarios in different stages of SAF industrialization. There are 8 game combinations among the three parties, namely, (A1 Active Response, G1 Supervision, F1 Active Production), (A1 Active Response, G1 Supervision, F2 Passive Production), (A1 Active Response, G2 Non-Supervision, F1 Active Production), (A1 Active Response, G2 Non-Supervision, F2 Passive Production), (A2 Passive Response, G1 Supervision, F1 Active Production), (A2 Passive Response, G1 Supervision, F2 Passive Production), (A2 Passive Response, G2 Non-Supervision, F1 Active Production), and (A2 Passive Response, G2 Non-Supervision, F2 Passive Production). As shown in Table 3.

Table 3

Table 3. Yield matrix of the three-party evolutionary game.

Note: (Supervision, Active Response, Active Production) is the ideal strategy combination for the development stage of SAF industrialization, (Nonsupervision, Active Response, Active Production) is the ideal market-oriented combination for the mature stage, and (Supervision, Passive Response, Passive Production) is the high-carbon lock-in dilemma combination for the initial stage of industrialization. The subsequent analysis focuses on “how to promote the transition of strategy combinations to the ideal state”.

3 Equilibrium analysis of the tripartite evolutionary game model and mapping to the SAF industrialization stage

To reveal the dynamic strategic interaction mechanism among the government, airlines, and fuel producers in the process of sustainable aviation fuel (SAF) industrialization and to clarify the core constraints of the tripartite strategy equilibrium and the key conditions for breaking these constraints at different industrialization stages, this section extracts core equations and equilibrium conditions on the basis of evolutionary game theory. By quantifying and analyzing the replicator dynamic equations and evolutionary stable strategies (ESSs) of the three subjects, the feedback path between policy tools and market responses is systematically interpreted, providing a theoretical basis for the subsequent design of phased industrialization strategies. (Note: All mathematical derivation processes are detailed in Supplementary Appendix A; the main text focuses on the framework-based presentation of key conclusions.

3.1 Replicator dynamic equations of the tripartite evolutionary game model

On the basis of constructing a tripartite evolutionary game model involving the government, airlines, and fuel producers, to further reveal the dynamic evolutionary mechanism underlying the strategy choices of each stakeholder, it is necessary to derive their replicator dynamics equations. As a core analytical tool in evolutionary game theory, the replicator dynamics equation describes the dynamic process of strategy adjustment among boundedly rational agents within a population. It reflects the behavioral regularity by which agents gradually shift toward more optimal strategies, based on a comparison between their current payoffs and the average payoffs of the population. By constructing the replicator dynamics equations for the three stakeholders, we can systematically characterize the internal driving forces and evolutionary paths of strategic interactions among the government, airlines, and producers during the industrialization of SAF. This provides a theoretical foundation for subsequent equilibrium stability analysis and phase mapping. As illustrated in Table 4.

Table 4

Table 4. Core equations and stability conditions of three-party evolutionary games.

Note: The derivation of replicator dynamic equations is provided in Supplementary Appendix A1. Note: The values of the core variables in the equations, such as government subsidies, carbon taxes, and airline investment costs, must comply with the constraints of the industrialization stage, and specific thresholds are verified through subsequent simulations.

3.2 Jacobian matrix of the replication dynamic system

The evolutionary stable equilibrium solutions can be obtained by calculating the partial stability of the Jacobian matrix. The Jacobian matrix is a matrix formed by arranging first-order partial derivatives in a specific manner, and its significance lies in that it represents the optimal linear approximation of a differentiable equation at a given point. The Jacobian matrix of the carbon emission reduction replication dynamic system is shown in Equation 1, where in which

$A=\left[\begin{array}{ccc}{a}_1& a_2& a_3\\ a_4& a_5& a_6\\ a_7& a_8& a_9\end{array}\right](1)$

$a_1=\frac{d\left(F\left(x\right)\right)}{dx}=\left(1-2x\right)\left[\begin{array}{l}\mathrm{y}\left(S_a+C_h\right)\\ +\mathrm{z}\left(\theta \times S_1\times R_3-C_t\right)\\ +\left(S_l-S_h-C_5+C_6+R_q+C_q\right)\end{array}\right]$

$a_2=\frac{d\left(F\left(x\right)\right)}{dy}=\mathrm{x}\left(1-\mathrm{x}\right)\left(S_a+C_h\right)$

$a_3=\frac{d\left(F\left(x\right)\right)}{dz}=\mathrm{x}\left(1-\mathrm{x}\right)\left(\theta \times S_1\times R_3-C_t\right)$

$a_4=\frac{d\left(F\left(y\right)\right)}{dx}=\mathrm{y}\left(1-\mathrm{y}\right)\left[-\left(S_a+C_h+E_5\right)\right]$

$a_5=\frac{d\left(F\left(y\right)\right)}{dy}=\left(1-2y\right)\left[\begin{array}{l}\mathrm{z}\left(C_a-C_b-S_m-E_4\right)\\ -\mathrm{x}\left(S_a+C_h+E_5\right)\\ +\left(\mathrm{W}+C_h+C_b-{\mathrm{C}}_{\mathrm{g}}+E_4+E_5\right)\end{array}\right]$

$a_6=\frac{d\left(F\left(y\right)\right)}{dz}=\mathrm{y}\left(1-\mathrm{y}\right)\left(C_a-C_b-S_m-E_4\right)$

$a_7=\frac{d\left(F\left(z\right)\right)}{dx}=\mathrm{z}\left(1-\mathrm{z}\right)\left[\left(1-\theta \right)S_1\times R_3+R_5-R_7\times S_1\right]$

$a_8=\frac{d\left(F\left(z\right)\right)}{dy}=\mathrm{z}\left(1-\mathrm{z}\right)\left(S_m+C_b-C_a\right)$

$a_9=\frac{d\left(F\left(z\right)\right)}{dz}=\left(1-2z\right)\left\{\begin{array}{l}\mathrm{x}\left[\begin{array}{l}\left(1-\theta \right)S_1\times R_3\\ +R_5-R_7\times S_1\end{array}\right]+\\ \mathrm{y}\left(S_m+C_b-C_a\right)+\left(R_7\times S-C_s\right)\end{array}\right\}$

3.3 Equilibrium points and stability analysis of the tripartite evolutionary game

On the basis of the eigenvalue analysis of the Jacobian matrix (detailed in Supplementary Appendix B, the system has 8 strategy equilibrium points (D1–D8), and their stability conditions are closely related to the stages of the industrial life cycle. Table 4 summarizes the stability conditions of key equilibrium points and their corresponding industrial mapping relationships. From the perspective of the SAF industry, the stability conditions are further analyzed in conjunction with industrial life cycle theory. The industrial life cycle refers to the period from the emergence of an industry to its complete withdrawal from socioeconomic activities. On the basis of relevant research on the industrial life cycle, this study divides the life cycle of the SAF industry into three stages: the initial stage, development stage, and maturity stage. With the goal of promoting the sustainable development of the SAF industry, this study selects and analyzes in detail the ESS corresponding to these three life cycle stages. As illustrated in Table 5.

Table 5

Table 5. Mapping the stability conditions of the equilibrium point in the three-party evolutionary game to the industrialization stage.

To improve interpretability, we further map key ESS inequalities into policy-relevant threshold conditions. For example, the conditions under which fuel producers evolve toward active SAF production can be rearranged into minimum requirements on (i) airlines’ upstream investment coverage ratio (e.g., C_t/C_s) and (ii) the profit-sharing parameter θ. These thresholds are derived directly from the payoff-dominance inequalities (active vs. passive production) and the corresponding ESS stability conditions for the development-stage equilibrium reported in Table 4.

3.4 Model derivation of key thresholds

On the basis of the replicator dynamic system and the local stability analysis of the Jacobian matrix, a set of policy-relevant threshold conditions can be derived that determine whether the system converges to the low-carbon equilibrium at each stage. In essence, these thresholds are obtained by comparing the expected payoffs of alternative strategies (active vs. passive) for each player and identifying the parameter values at which the payoff differences change sign.

3.4.1 Investment cost coverage rate of 33.3%

The expected payoff difference of fuel producers between active production (F1) and passive production (F2), in the development stage, can be written as:

$\Delta \pi ={\pi }_{F_1}-{\pi }_{F_2}=\left[\left(1-\theta \right)S_1\times R_3+S_2\times R_4+R_7\times \left(S-S_1\right)+S_m+R_5-C_s-C_a\right]-\left(S_2\times R_4-C_b\right)(2)$

Active production is favored when $\Delta \pi \ge 0$. At this time, by simplification, we get:

$\frac{C_t}{C_S}\ge \frac{S_1{R}_3\left(\theta -1\right)+R_7\left(S_1-S\right)-S_m+C_s+C_a-C_b}{C_s}(3)$

When the investment cost increases from 15 to 30, at this time, $\frac{C_t}{C_S}\ge 33.3\%$, the replicator dynamic equation of the manufacturer’s active strategy turns from negative to positive. Hence the 33.3% threshold.

3.4.2 Profit sharing ratio of 15%

The expected payoff difference of airline between active response (A) and passive response (P), in the development stage, can be written as:

$\Delta \pi ={\pi }_A-{\pi }_P=\left(S_l+S_a+R_q+\theta \times S_1\times R_3-C_5-C_t\right)-\left(S_h-C_q-C_6-C_h\right)(4)$

Active response is stable when $\Delta \pi \ge 0$. At this time, by simplification, we get:

$\theta \ge \frac{C_5+C_t+S_h-S_l-S_a-R_q-C_q-C_6-C_h}{S_1{R}_3}(5)$

Including SAF premium, carbon-quota benefits, and profit-sharing θ, substituting development-stage parameters gives: $\theta \ge 15\%$.

Thus airlines must receive $\theta \ge 15\%$ of SAF profit for active response to be payoff-superior.

3.4.3 Carbon tax intensity of 1.6 times

The expected net income difference of airline between active production (A) and passive production (P), in the development stage, can be written as:

$\Delta \pi ={\pi }_A-{\pi }_P=\left[\left(1-\theta \right)S_1\times R_3+S_2\times R_4+R_7\times \left(S-S_1\right)+S_m+R_5-C_s-C_a\right]-\left(S_2\times R_4-C_b\right)(6)$

Stability requires $\Delta \pi \ge 0$. At this time, by simplification, we get:

$\frac{C_b}{C_a}\ge \frac{\left(\theta -1\right)S_1{R}_3+\left(S_1-S\right)R_7-S_m-R_5+C_s+C_a}{C_a}(7)$

When in the development stage, the TAF carbon tax rate levied when the manufacturer produces passively, compared with the TAF carbon tax rate levied when producing actively, jumps from 3 in the initial stage to 1.6 with reference to Table 6.

Table 6

Table 6. Parameter value table by phase.

Rearranging yields: $\frac{{\pi }_A}{{\pi }_P}\ge 1.6$.

Thus passive-production TAF must face more than 1.6 times carbon tax relative to active-production conditions.

3.4.4 Carbon quota price threshold of 0.75 in the mature stage

The expected Carbon Quota Price difference of airline between active production (A) and passive production (P), in the development stage, can be written as:

$\Delta \pi ={\pi }_A-{\pi }_P=\left(S_l+R_q+\theta \times S_1\times R_3-C_5-C_t\right)-\left(S_h-C_q-C_6\right)(8)$

Airlines adopt active response when $\Delta \pi \ge 0$. At this time, by simplification, we get:

$\frac{R_q}{C_q}\ge \frac{C_5+C_t+S_h-C_q-C_6-S_l-\theta S_1{R}_3}{C_q}\ge 0.75(9)$

Therefore, carbon-quota selling price must be at least 75% of its purchase cost for active SAF adoption in a subsidy-free mature market, to make $\Delta \pi \ge 0$, when $P_c$ satisfies, the replicator dynamic equation of the airline’s active strategy turns from negative to positive.

4 Phased numerical simulation of SAF industrialization

4.1 Explanation of the basis for parameter setting

4.1.1 Support from real industry data

The initial assignment of parameters in this study comprehensively refers to the 2023 Global SAF Industry Report, policy documents of the Civil Aviation Administration of China, and existing academic research results. For example, the International Energy Agency (IEA)’s 2023 Aviation Fuel Tracking Report and the International Air Transport Association (IATA)’s SAF Market Analysis indicate that the production cost of SAF is 3.1 times that of traditional aviation fuel, the global SAF production capacity accounts for only 0.1% of the total aviation fuel demand, and the coverage rate of SAF refueling facilities at airports is only 11.8%. This study also refers to the 14th Five-Year Special Plan for the Green Development of Civil Aviation and the Outline for the Development of the Green Aviation Manufacturing Industry (2023–2035), which clearly set the goal of reaching 20,000 tons of SAF consumption by 2025 and proposes subsidy and supervision requirements for SAF R&D and demonstration applications. The values of the relevant parameters (such as government subsidies and carbon taxes) are set accordingly. The parameter-setting logic for SAF promotion in the evolutionary game model studied by Zhang et al—including the value range of core variables such as airline procurement premiums and government supervision benefits, as well as sensitivity analysis methods—provides theoretical support for parameter assignment in this study.

It should be noted that current SAF support policies are often implemented as pilots and may vary substantially across regions and time. Therefore, in our simulations, subsidy-related parameters are interpreted as policy intensity scenarios rather than a single observed constant in reality. This design allows us to represent heterogeneous government willingness and fiscal constraints and to examine how different subsidy intensities shift the evolutionary outcomes across stages.

4.1.2 Adaptability to theoretical models

The tripartite evolutionary game model constructed in this study is based on the assumption of bounded rationality, and the parameter settings must meet the following theoretical requirements: The strategic choices of the subjects are based on benefit‒cost comparisons; policy variables (such as subsidies and carbon taxes) must have phased differences to reflect the characteristics of the industrial life cycle; and the parameter values must ensure the solvability and stability of the replicator dynamic equations and the Jacobian matrix. All the parameters are calibrated using real data and ensure clear economic implications and mathematical consistency within the theoretical framework.

4.1.3 Consistency with simulation results

The numerical simulation results after parameter setting show that the system converges to equilibrium point D3 (passive response, supervision, and passive production) in the initial stage, which is highly consistent with the actual situation of the SAF industry in the introduction period: although the government has established a supervision framework, subsidies are limited and fail to effectively incentivize producers to transform; producers tend to passively produce traditional aviation fuel because of high R&D costs and low export benefits; and airlines choose a passive response because of high SAF procurement costs. As shown in Table 6.

These simulation results are consistent with the actual industrial phenomenon of “limited initial policy effects and lukewarm market response”, which reversely verifies the rationality and feasibility of the parameter settings.

4.2 Dynamic evolution of stakeholders at different stages

Following industrial life-cycle logic and SAF deployment realities, we classify SAF industrialization into three stages—initial, development, and mature—based on observable characteristics: cost premium and technology uncertainty (high→moderate → low), production scale and supply capacity (low→expanding→stable), infrastructure and certification readiness (limited→improving→ established), and market mechanism maturity (policy-driven pilots→mixed policy–market→ market-based with carbon mechanisms). To operationalize this classification, we map each stage to stage-specific parameter constraints (e.g., relative cost terms, capacity/supply parameters, and policy intensity settings) and link them to the corresponding ESS outcomes (Table 4).

Table 7 summarizes the stage-defining features and their parameter implications, ensuring that the stage classification is not purely conceptual but is explicitly connected to the model and simulation settings.

Table 7

Table 7. Stage features and parameter implications.

4.2.1 Dynamic evolution of stakeholders in the initial stage

The initial stage corresponds to an early deployment regime in which SAF faces a large cost premium relative to conventional jet fuel, production capacity is limited, and infrastructure/certification readiness is incomplete. In such a regime, market signals alone are insufficient to trigger a self-sustaining SAF supply chain; instead, outcomes are predominantly driven by policy intervention and compliance pressure. Accordingly, in the initial-stage parameter setting, we reflect (i) high technology/R&D and scale-up costs for fuel producers, (ii) limited effective supply capacity, and (iii) a policy environment in which government subsidies and regulatory enforcement play a decisive role.

Under these conditions, the replicator dynamics indicate that without sufficiently strong policy incentives, the system tends to lock into a low-SAF equilibrium (airlines remain conservative; producers stay passive; government prefers non-supervision due to fiscal burdens and limited short-term returns). When subsidy intensity and/or carbon-related regulatory pressure increase beyond critical levels, the system transitions toward an active-promotion equilibrium, meaning that the government’s entry into supervision becomes evolutionarily stable and producers are incentivized to initiate SAF industrialization. This stage-specific result should be interpreted as a policy-led unlocking mechanism rather than a purely mathematical convergence.

The initial parameter values are as follows:

$C_1=6,C_2=2,C_s=90,R_1=6.5,R_2=2.5,R_3=0.5,R_4=0.5,R_5=30,R_6=7.5,R_7=1,S=10,S_m=15,M_1=70,M_2=80,P_t=2.5,E=0.4,C_a=70,C_b=80,C_3=7,C_4=3,C_t=30,\theta =0.05,S_1=3.6,S_2=5.4,S_3=0,S_4=9,C_5=41.4,C_6=27,C_h=30,S_a=15,S_h=100,S_l=30,C_g=7.2,\mathrm{W}=30,E_1=19.8,E_2=27,E_3=22.4,P_c=5,R_q=13,C_q=23,a=1,b=3,\mathrm{P}=5,E_4=160,E_5=135$

According to the tripartite evolutionary game trajectory in Figure 2 below, the system finally converges to the equilibrium point. The evolutionary stable strategy (ESS) combination in the initial stage is {passive response, supervision, passive production}. During the introduction period of the SAF industry, although the government has established a basic supervision framework, R&D subsidies are limited (see parameter values), resulting in fuel producers tending to passively produce traditional aviation fuel for a long period of time because of high R&D costs and insufficient export benefits; airlines quickly converge to a passive response because of the excessively high procurement cost of the SAF.

Figure 2

Figure 2. System evolution diagram during the initial stage.

The simulation results reveal that when the carbon quota cost of traditional fuel is significantly lower than the SAF emission reduction benefits and when the government’s environmental governance burden far exceeds the supervision benefits, the three parties form a negative “supervision-high carbon lock-in” equilibrium, which confirms the market failure characteristics of the period of SAF introduction.

4.2.2 Dynamic evolution of stakeholders in the development stage

The development stage represents a scale-up regime in which pilot deployment has already demonstrated SAF feasibility, but large-scale commercialization remains constrained by (i) capacity expansion financing, (ii) demand certainty, and (iii) upstream technology and scale-up risks borne by fuel producers. Compared with the initial stage, learning effects begin to reduce unit costs, yet producers still face substantial R&D and commercialization expenses, making bilateral coordination and credible off-take commitments essential. Therefore, the binding constraint in this stage shifts from “whether to start SAF” to “how to coordinate investment and risk-sharing to scale SAF reliably.”

To make the development-stage results operational, we report the implied threshold conditions that emerge from the development-stage ESS stability inequalities and the payoff-dominance condition for producers’ active SAF production (see Tables 2, 3). Specifically, the model implies that producers’ active production becomes evolutionarily stable only when airlines provide sufficient upstream participation—captured by a minimum R&D cost-sharing coverage ratio and a minimum profit-sharing ratio. Consistent with the headline findings reported in the abstract and conclusion, the development-stage simulations indicate that airlines’ contribution needs to reach at least 33.3% of producers’ SAF R&D costs and the profit-sharing ratio needs to be at least 15% for the system to converge to the high-SAF equilibrium. The detailed algebraic derivation and numerical substitution under the development-stage parameter set are provided in Section 3.4.

The parameter values are as follows:

$C_1=6,C_2=2,C_s=90,R_1=6.5,R_2=2.5,R_3=0.5,R_4=0.5,R_5=30,R_6=7.5,R_7=1,S=30,S_m=30,M_1=50,M_2=80,P_t=2.5,E=0.4,C_a=50,C_b=80,C_3=7,C_4=3,C_t=30,\theta =0.15,S_1=5.4,S_2=3.6,S_3=0,S_4=9,C_5=48.6,C_6=27,C_h=30,S_a=30,S_h=90,S_l=45,C_g=10.8,\mathrm{W}=25,E_1=16.2,E_2=27,E_3=20.6,P_c=5,R_q=22,C_q=32,a=1,b=3,\mathrm{P}=3,E_4=96,E_5=61.8$

The tripartite evolutionary trajectory in Figure 3 shows that the system finally converges to the equilibrium point. In the development stage, the ESS combination of the government, airlines, and producers is {Active Response, Supervision, Active Production}. When the government implements carbon taxes and differentiated supervision and provides R&D subsidies for the SAF, producers shift from traditional aviation fuel production (F2) to SAF production (F1). With the popularization of the SAF scale effect and the airline “off-take agreement + equity investment” model, the airline strategy quickly shifts from a passive response (A2) to an active response (A1).

Figure 3

Figure 3. System evolution diagram of development stages.

The simulation results reveal that when the SAF emission reduction benefits exceed the carbon cost of traditional fuel and the government’s supervision benefits cover its costs, the three parties form a positive feedback mechanism, realizing the transformation from being policy driven to being market driven.

In the development stage, the most effective policy role shifts from blanket subsidies toward reducing coordination frictions and de-risking private investment (e.g., standardized long-term offtake contracts, credit enhancement/guarantees, and credible carbon-policy signals).

4.2.3 Dynamic evolution of stakeholders in the mature stage

The mature stage reflects a regime in which SAF supply capacity and infrastructure are relatively established, unit costs have been reduced substantially, and policy objectives can be achieved increasingly through market-based mechanisms rather than continuous direct subsidies. In this stage, carbon pricing and quota/credit trading become the dominant drivers of strategic behavior: airlines internalize emissions costs more fully, and fuel producers can rely on stable demand and monetizable carbon value to sustain active SAF production.

Consistent with this logic, the mature-stage simulations indicate that a key stability condition is the viability of carbon quota/credit monetization (e.g., the selling price of carbon allowances being sufficiently close to the purchase cost). When the carbon market provides an adequate and predictable price signal, the system converges toward a stable high-SAF equilibrium with reduced need for heavy fiscal subsidies. Conversely, if carbon prices are too low or too volatile, the system may regress toward a weaker adoption equilibrium even in a technically mature environment, highlighting the importance of market-design quality and regulatory credibility.

The parameter values are as follows:

$C_1=6,C_2=2,C_s=70,R_1=6.5,R_2=2.5,R_3=0.5,R_4=0.5,R_5=30,R_6=7.5,R_7=1,S=50,S_m=15,M_1=30,M_2=80,P_t=0.5,E=0.4,C_a=6,C_b=16,C_3=7,C_4=3,C_t=27,\theta =0.15,S_1=7,S_2=2,S_3=0,S_4=9,C_5=55,C_6=27,C_h=15,S_a=15,S_h=70,S_l=65,C_g=14,\mathrm{W}=10,E_1=13,E_2=27,E_3=19,P_c=5,R_q=30,C_q=40,a=1,b=3,\mathrm{P}=0.5,E_4=16,E_5=13.5$

According to the tripartite evolutionary game trajectory in Figure 4, the system finally converges to the equilibrium point. In the mature stage of the SAF industry (with a certain market scale), the ESS combination of the three parties is {Active Response, Nonsupervision, Active Production}. At this time, driven by scale effects and export profits, producers independently choose the active production strategy, and their R&D costs are recovered through SAF sales and airline investments; airlines achieve a fundamental transformation in their operating cost structure because of increased carbon quota benefits, and their strategy stabilizes at active response.

Figure 4

Figure 4. System evolution diagram at the mature stage.

The simulation results show that when the government’s revenue after dynamic adjustment is lower than the market-oriented emission reduction benefits and when the net benefit of active production for producers is greater, the three parties form a “market-driven-policy withdrawal” equilibrium. This finding highlights the SAF industry’s ultimate transformation from government-led to market-oriented independent emission reduction. At this point, the unit emission reduction benefit of the SAF significantly exceeds the carbon quota cost of traditional fuel, and compared with the development stage, the government’s environmental governance costs decrease by 53.2%, verifying the effectiveness of market-oriented mechanisms.

5 Sensitivity analysis of the key factors for SAF industrialization

To identify the key policy variables and their critical values that affect the tripartite strategy transition at different industrialization stages, sensitivity analysis is conducted on the core parameters in the initial, development, and maturity stages. The aim is to clarify the minimum policy thresholds and risk boundaries for industrialization breakthroughs at each stage, providing a precise basis for phased policy design.

5.1 Sensitivity analysis of SAF industrialization in the initial stages

The core constraints of SAF industrialization in the initial stage are “high costs, zero supply, and no demand”. Therefore, “subsidies-carbon taxes” are selected as sensitivity factors. These two factors directly affect the cost–benefit structure of both the supply and demand sides by “reducing green premiums” and “increasing the cost of traditional fuels”, respectively, and are key policy tools to break the “high carbon lock-in”.

According to the numerical simulation results in Figures 5, 6, the system tends to fall into the “high carbon lock-in” dilemma in the initial stage. The following presents scenario simulation results under different intensity policy combinations:

Figure 5

Figure 5. Influence of multi-parameters on the behavior evolution of stakeholders in the initial stage.

Figure 6

Figure 6. Influence plane diagram of multi-parameters on the behavior evolution of stakeholders in the initial stage.

Low-intensity combination: Fails to break the cost threshold, and the system is locked in the D3 equilibrium point of “airline passive response-government supervision-producer passive production,” indicating typical market failure.

Medium-intensity combination: Although high subsidies (which cover 208.3% of the cost difference) can drive airlines to shift to an active response, producers still face negative benefits from the active production strategy (see parameter calculation) because of high R&D costs (see parameter values), insufficient scale effects (see parameter values), and low export benefits (see parameter values). This benefit is lower than that of passive production (see parameter calculation), resulting in blocked transformation on the supply side and the formation of an unstable state of “producer passive production-airline active response-government continuous supervision”.

High-intensity combination: Only the combination of “high subsidies (accounting for 66.7% of R&D costs) + high punitive carbon taxes (see parameter values)” can forcefully break the “high carbon lock-in”. Ultrahigh carbon taxes (see parameter values) significantly increase the production cost of traditional fuels (see parameter calculation), making the loss of producers choosing active production still less than that of choosing passive production (see parameter comparison), leading to a “choosing the lesser of two evils” decision. Moreover, high subsidies (see parameter values) completely reverse the cost disadvantage of airlines (see parameter calculation), driving them to respond actively. The government chooses continuous supervision because its supervision benefits (see parameter values) are far greater than its nonsupervision benefits (see parameter values). Driven by strong policies, the three parties form positive feedback and finally converge to the D8 equilibrium point of “all active.”

5.2 Sensitivity analysis of SAF industrialization in the development stages

The core constraint of SAF industrialization in the development stage is “weak supply-demand coordination”. Therefore, “investment, profit-sharing ratio, and carbon tax” are selected as sensitivity factors. At this stage, “off-take agreements” directly lock in demand and stabilize producers’ investment expectations; “carbon taxes” continuously increase the cost of traditional paths. These two factors work together to solve the “supply-demand mismatch” problem.

According to the numerical simulation results in Figures 7, 8, the goal in the development stage is to promote the system to transition to the “all active” equilibrium. The following presents scenario simulation results under different intensity policy combinations:

Figure 7

Figure 7. Influence of multi-parameters on the behavior evolution of stakeholders in the development stage.

Figure 8

Figure 8. Influence plane diagram of multi-parameters on the behavior evolution of stakeholders in the development stage.

Low-intensity combination: Although subsidies can drive airline demand, the penalties for producers producing traditional fuels are seriously insufficient. The benefit of producers choosing passive production (see parameter calculation) is greater than that of active production (see parameter calculation), resulting in unmet procurement demand for airlines (see parameter values) and supply–demand disruption. Although the government maintains supervision (see parameter values), policy tool coordination fails.

Medium-intensity combination: The increased policy intensity (see parameter values) begins to show effects, forming an effective “push” for producers: carbon taxes (see parameter values) increase the cost of traditional fuels (see parameter calculation), and airlines increase investment in fuel producers (see parameter values), making the loss of producers choosing active production (see parameter calculation) significantly less than that of passive production (see parameter calculation) and prompting their transformation. Airlines maintain an active response (see parameter values) because of the improved economy from subsidies (see parameter values) and potential carbon quota benefits (see parameter values). Airlines lock in part of the demand (see parameter values, accounting for 18% of production capacity) through “off-take agreements”, forming initial strategic coupling with producers. The system successfully transitions to the target equilibrium point, but this state may face fluctuation risks under external shocks.

High-intensity combination: Policy intensity is further enhanced (see parameter values). The “push” mechanism (high carbon taxes) and “pull” mechanism (large-scale investment by airlines in fuel producers) jointly drive producers to transform (see parameter values). Airlines form in-depth binding with producers through “off-take agreements + equity investment (in exchange for 25% profit sharing)”, establishing a stable “demand-supply-investment” closed loop. Moreover, the role of the carbon quota trading mechanism (see parameter values) becomes prominent, making the emission reduction benefits of the SAF (see parameter calculation) gradually gain advantages over the quota cost of traditional fuels (see parameter calculation) and guiding the market to spontaneously choose low-carbon paths. The government maintains supervision because its supervision benefits (see parameter values) are higher than costs (see parameter values). The system quickly and stably converges to the D8 equilibrium point, completing the initial transition from being policy driven to being market driven.

5.3 Sensitivity analysis of SAF industrialization in the mature stages

In the mature stage, the main challenge of SAF industrialization lies in “policy dependence”. The government has shifted to service support to achieve sustainable industrialization. Therefore, this study identifies the “carbon quota price” as the key sensitivity factor. Moreover, there is a link between the carbon quota price and actual aviation fuel procurement costs. Therefore, in addition to reasonable procurement prices, carbon price factors must be considered when analyzing how they affect airlines’ economic motivation to adopt the SAF. The carbon quota price directly determines the marginal benefit structure of airlines and is a core market adjustment mechanism to maintain the system’s tendency toward the “all active” equilibrium without policy intervention.

According to the numerical simulation results in Figures 9, 10, the goal in the mature stage is to achieve a self-sustaining equilibrium driven by the market. The following presents scenario simulation results under different carbon quota prices:

Figure 9

Figure 9. Influence of multi-parameters on the behavior evolution of stakeholders in the mature stage.

Figure 10

Figure 10. Influence plane diagram of multi-parameter on the behavior evolution of stakeholders in the mature stage.

Low-carbon quota price scenario: Scale effects (see parameter values) and market-oriented benefits become dominant. Producers can obtain positive benefits from active production (see parameter calculation) because of significant scale effects (see parameter values), airline equity investment support (see parameter values), and reduced R&D costs (see parameter values), which is better than passive production (see parameter calculation). Airlines maintain an active response because of increased carbon quota trading benefits (see parameter calculation; even with procurement costs (see parameter values), the net benefit under the active strategy is still significantly better than that under the passive strategy) and a sustainable subsidy mechanism (see parameter values). The key change lies with the government: the supervision cost (see parameter values) plus the subsidy expenditure (see parameter values) has exceeded the supervision benefit (see parameter values), leading the government to choose to withdraw from supervision (see parameter values). The system successfully converges to the target equilibrium point, forming a self-sustaining market closed loop, and environmental governance costs (see parameter values) decrease significantly by 53.2% compared with the development stage.

Medium carbon quota price scenario: Market-driven characteristics become more prominent. Airlines (see parameter values) and producers (see parameter values) can maintain active strategies on the basis of market benefits (carbon quota benefits, scale production benefits, and investment returns), with further reduced dependence on policies. The conditions for the government to withdraw from supervision (see parameter values) become more solid. The medium-intensity carbon quota price mainly plays a role in accelerating convergence and providing moderate support, and the system operates stably in the ideal D7 state.

High-carbon quota price scenario: Market forces are already sufficiently strong and efficient. The benefit structure of airlines (see parameter values) and producers (see parameter values) firmly supports their active strategies. The net benefit of government supervision (see parameter calculation) is far lower than that of nonsupervision (see parameter calculation); thus, withdrawal (see parameter values) is an economically rational choice. Notably, the high-intensity carbon quota policy (see parameter values) does not change the final equilibrium result D7 of the system but causes potential waste of fiscal resources (policy redundancy), highlighting the efficiency and self-organization ability of market mechanisms in the mature stage.

5.4 Analysis of the SAF phase transition mechanism

High-intensity subsidies in the initial stage stimulate airlines’ procurement through “cost reduction”, thereby driving fuel producers’ capacity expansion and laying the foundation for “supply-demand coordination” in the development stage.

In the development stage, the “equity investment + profit sharing” mechanism enables airlines to lock in long-term demand, while fuel producers secure stable cash flow to amortize R&D costs. This pushes the price of SAF to approach the critical threshold of 2.25 times that of traditional jet fuel.

In the mature stage, pricing is market-driven. Airlines can cover costs through emission reduction benefits and proactively purchase. Producers maximize economies of scale to reduce costs to a level close to that of traditional aviation fuel. The key lies in the ratio of carbon allowance price to cost being ≥0.75, after which the government withdraws from supervision and shifts to a service- oriented role. The stage characteristic is that the market can achieve sustainable development independently. As shown in Figure 11 below.

Figure 11

Figure 11. Schematic diagram of phase transition path.

6 Conclusion

With respect to three core stakeholders—governments, airlines, and fuel producers—this study develops a tripartite evolutionary game model. Guided by the industry life cycle theory, the development of the SAF industry can be divided into three phases: initial, developmental, and mature. Through theoretical deductions and numerical simulations, three key conclusions are derived.

1. In the initial stage of SAF industrialization, the system spontaneously converges to the “high carbon lock-in” state Supervision, Passive Response, Passive Production), reflecting that the market itself is unable to break through the structural bottlenecks of “high costs, lack of supply, and insufficient demand”. At this stage, the government can effectively cover the cost gap of airlines and incentivize producers’ capacity investment through the combined intervention of “high-intensity subsidies (covering more than 66.7% of SAF procurement premiums)+high punitive carbon taxes”, addressing the cost pain points in the industrialization introduction period and forcing the system to transition to a low-carbon path.

2. In the development stage of SAF industrialization, whether the system can transition to the low-carbon equilibrium of (Active Response, Supervision, Active Production) depends on the synergy between policies and market mechanisms. This study shows that the key policy thresholds for realizing the system transition at this stage are as follows: the investment cost of airlines in producers must cover ≥33.3% of SAF R&D costs; the profit-sharing ratio that airlines obtain from producers through off-take agreements must be no less than 15%; and the carbon tax intensity must be increased to more than 1.6 times that of the passive production of traditional aviation fuel (TAF) compared with active production. Below this threshold combination, the system is unable to stably converge to a low-carbon equilibrium and faces the structural bottleneck of “weak supply-demand coordination”. At this stage, a “push-pull” dual-drive system should be built through mechanisms such as “green finance support + off-take agreements + industrial chain equity investment”: on the one hand, continuously increase the cost of traditional fuels through carbon taxes and, on the other hand, stabilize supply‒demand relationships through profit sharing and long-term procurement agreements to address the “supply-demand mismatch” pain point and promote the large-scale expansion of SAF production capacity.

Practically, such upstream risk-sharing arrangements are more feasible for large full-service carriers or hub carriers (including those with stronger capital capacity and policy-driven mandates), whereas smaller or low-cost carriers may face tighter cash-flow constraints and risk tolerance, making direct cost-sharing less likely.

3. In the mature stage of SAF industrialization, the system can stabilize at the market-oriented equilibrium of (Active Response, Nonsupervision, Active Production). At this time, the carbon quota trading mechanism and emission reduction benefits become the core driving forces, and the SAF has cost competitiveness and scale effects. The key driving factors for maintaining this market-oriented equilibrium are as follows: the selling price of carbon quotas must be at least 0.75 times the purchase cost, and the price ratio of airlines’ procurement of SAF to TAF should be maintained at approximately 2.25 to be reasonable. Below the above thresholds, the system’s ability to resist external shocks weakens, reflecting the existence of structural shortcomings in the market-oriented internal driving force. The government should shift its role from “leading supervision” to “service support”, focusing on maintaining market order by improving carbon market rules and establishing a full-life cycle carbon emission evaluation system, addressing the “lack of market mechanisms” pain point, and completing the ultimate transformation of the SAF from “policy dependence” to “market autonomy.”

These conclusions provide a theoretical basis for clarifying the government’s positioning, policy tool selection, and coordination of market entities’ behaviors in promoting SAF industrialization. On the basis of the research results and combined with international practices, the following policy recommendations are proposed from three aspects: policy coordination, market construction, and mechanism design.

1. Construct a dynamic policy combination accurately matched with the industrialization stage. In the initial stage, implement “high-intensity subsidies and carbon taxes” to force breakthroughs, with a focus on addressing cost and supply bottlenecks; in the development stage, promote “subsidy reduction + carbon market deepening + off-take agreement promotion” to achieve dual-drive by policies and markets; in the mature stage, gradually withdraw subsidies, strengthen market-oriented mechanisms such as carbon emission rights trading and green certification, and promote the government’s role transformation from a leader to a service provider.

2. Focus on promoting the implementation of the “long-term off-take agreement + industrial chain equity investment” mechanism in the development stage. Encourage airlines and fuel producers to sign procurement agreements of more than 10 years to stabilize demand expectations; support airlines to participate in upstream production through equity investment and share benefits throughout the life cycle, enhancing the collaborative resilience of the industrial chain and addressing the supply‒demand mismatch problem.

3. In view of China’s SAF industry currently being in the critical transition period from the “initial stage” to the “development stage”, adopting a transitional strategy of “gradual subsidy reduction + mandatory off-take agreements” is recommended. At this stage, it is necessary to maintain subsidy intensity to sustain confidence in capacity investment, while mandating large airlines sign SAF off-take agreements through pilot policies to stimulate the enthusiasm of the production side; simultaneously improve the carbon market mechanism and incorporate SAF emission reductions into the carbon emission rights trading system to provide institutional guarantees for comprehensive entry into the development stage after 2025.

This study provides a precise policy design framework based on theoretical deduction and empirical simulation for China’s SAF industrialization, clarifying the critical thresholds of policy intervention and transformation paths at each stage, which helps to avoid blindness and short-sightedness in policy formulation and provides a scientific decision-making basis for managers. Future research can further introduce practical factors such as international competition, passengers’ green preferences, and energy price fluctuations to enhance the applicability and predictability of the model, providing more systematic and detailed decision support for promoting the development of SAF industrialization.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

LZ: Conceptualization, Validation, Supervision, Writing – review and editing. XW: Software, Formal Analysis, Writing – original draft, Visualization, Methodology, Data curation, Investigation. PZ: Investigation, Conceptualization, Supervision, Project administration, Funding acquisition, Writing – review and editing. WJ: Writing – original draft, Investigation, Conceptualization, Methodology.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the China Civil Aviation Safety Capability Project, grant number ASSA2020/08 and the Fundamental Research Funds for the Central Universities (Institute of Civil Aviation Management Science and Engineering, grant number TD2025DS03).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1731253/full#supplementary-material

Footnotes

^{Abbreviations:}SAF, Sustainable Aviation Fuel.

References

Cathay Pacific (2022). Investment in gevo to accelerate sustainable aviation fuel development. Available online at: https://www.cathaypacific.com/sustainability/fuel.html.

Google Scholar

Chen, L., Zhang, Y., and Liu, Q. (2022). Multilateral game behavior and mechanism of international aviation carbon emissions reduction under the carbon neutral vision. Chin. J. Popul. Resour. Environ. 20, 231–238. doi:10.1016/j.cjpre.2022.06.001

CrossRef Full Text | Google Scholar

Civil Aviation Administration of China (CAAC). (2022). 14th five-year special plan for the green development of civil aviation.

Google Scholar

Cui, Q., Lei, Y. L., Jia, Z. K., Wang, Y., and Li, Y. (2024). Path analysis for controlling climate change in global aviation. iScience 27, 108726. doi:10.1016/j.isci.2024.110126

PubMed Abstract | CrossRef Full Text | Google Scholar

Delta Air Lines (2022). Delta and gevo announce 10-year sustainable aviation fuel agreement. Available online at: https://news.delta.com/delta-and-gevo-announce-10-year-sustainable-aviation-fuel-agreement.

Google Scholar

Dray, L., Schäfer, A., and Al Zaini, A. (2021). Airline compliance with the carbon offsetting and reduction scheme for international aviation (CORSIA): an evolutionary game approach. Transp. Res. Part D Transp. Environ. doi:10.1016/j.trd.2021.103027

CrossRef Full Text | Google Scholar

European Commission (2023). Commission implementing regulation (EU) 2023/240 on ReFuelEU aviation rules. Official J. European Union L 33, 1–129. Available online at: https://eur-lex.europa.eu/eli/reg_impl/2023/240.

Google Scholar

Fortune Golden Key (2024). Inventory of the latest SAF projects in China 2024. Available online at: https://business.sohu.com/a/824100542_121123885.

Google Scholar

Graver, B., Rutherford, D., and Zheng, S. (2020). CO₂ emissions from commercial aviation: 2013, 2018, and 2019. Int. Counc. Clean Transp. doi:10.18730/4Q8MC

CrossRef Full Text | Google Scholar

Guo, W., Chen, S., and Lei, M. (2023). Evolutionary game and strategy analysis of carbon emission reduction in supply chain based on system dynamic model. Sustainability 15, 8933. doi:10.3390/su15118933

CrossRef Full Text | Google Scholar

He, Y., Chen, Z. H., and Liao, N. (2022). The impact mechanism of government subsidy approach on manufacturer’s decision-making in green supply chain. Chin. J. Manag. Sci. 29, 88–98. doi:10.16381/j.cnki.issn1003-207x.2019.1854

CrossRef Full Text | Google Scholar

Hong Kong Environmental Protection Department (2023). Guideline on carbon allowance allocation for aviation sector 2024-2026. Available online at: https://www.epd.gov.hk/epd/english/climate_change/files/Aviation_Carbon_Allowance.pdf.

Google Scholar

International Air Transport Association (IATA) (2021). Net zero by 2050: a pathway for aviation. Available online at: https://www.iata.org/contentassets/d13875e9f7844d6a97b0c8f8f3d72f03/net-zero-by-2050.pdf.

Google Scholar

International Air Transport Association (IATA). (2023). SAF infrastructure gap remains major barrier to decarbonization.

Google Scholar

International Energy Agency (IEA). (2023a). Aviation tracking report 2023. Available online at: https://www.iea.org/reports/aviation; 2023.

Google Scholar

International Energy Agency (IEA). (2023b). Net zero by 2050: a roadmap for the energy sector.

Google Scholar

Karanki, F., and Yu, C. (2025). Assessing airports’ bargaining power: evidence from U.S. airports using a two-tier stochastic frontier analysis. J. Air Transp. Manag. 102667. doi:10.1016/j.jairtraman.2025.102667

CrossRef Full Text | Google Scholar

Li, J. H., Wang, Y., Zhang, L., Liu, W., and Chen, X. (2022). Study on the policy coordination mechanism in China's carbon market construction. China Environ. Sci. 42, 1912–1921. doi:10.19674/j.cnki.issn1000-6923.20220210.003

CrossRef Full Text | Google Scholar

Liu, G. F., and Sheng, W. J. (2023). Pricing and carbon emission reduction decision in dual-channel supply chain with carbon tax and consumer preference. Proc. 2023 2nd Int. Conf. Inf. Syst. Softw. Eng. 4, 106. doi:10.54254/2755-2721/4/2023106

CrossRef Full Text | Google Scholar

Liu, X., Sun, Y., and Wang, J. (2023). Dynamic game analysis of carbon market mechanism in civil aviation. Chin. J. Manag. Sci. 15, 44–52. doi:10.16868/j.cnki.1674-6252.2023.03.044

CrossRef Full Text | Google Scholar

Liu, H., Xiang, H., Wang, B., Zeng, P., Deng, C., Xia, Z., et al. (2025). Current situation analysis and prospects of sustainable aviation fuel. J. Environ. Chem. Eng. 13, 111666. doi:10.1016/j.jece.2024.111666

CrossRef Full Text | Google Scholar

Ministry of Industry and Information Technology, Ministry of Science and Technology, Ministry of Finance, and Civil Aviation Administration of China (CAAC). (2023). Outline for the development of green aviation manufacturing industry (2023-2035).

Google Scholar

National Development and Reform Commission, and National Energy Administration. (2022). 14th five-year plan for the modern energy system.

Google Scholar

Neste Corporation (2023). Neste invests in new renewable products refinery in rotterdam to serve growing demand. Available online at: https://www.neste.com/releases-and-news/expansion/neste-invests-new-renewable-products-refinery-rotterdam-serve-growing-demand.

Google Scholar

Parry, I. W. H., Williams, R. C., and Goulder, L. H. (1999). When can carbon abatement policies increase welfare? The fundamental role of distorted factor markets. J. Environ. Econ. Manag. 37, 52–84. doi:10.1006/jeem.1998.1058

CrossRef Full Text | Google Scholar

Sasol & Talos Energy (2023). U.S. sustainable aviation fuel project under the inflation reduction act (IRA). Available online at: https://www.sasol.com/investor-relations/news-media-releases.

Google Scholar

Schäfer, A. W., Barrett, S. R. H., Doyme, K., Dray, L. M., Gnadt, A. R., Self, R., et al. (2019). Technological, economic and environmental prospects of all-electric aircraft. Nat. Energy 4, 160–172. doi:10.1038/s41560-019-0296-1

CrossRef Full Text | Google Scholar

Smith, A. J., Johnson, B. R., Li, C., and Gupta, R. (2023). Feedstock constraints and sustainability challenges for scalable sustainable aviation fuel production. Nat. Sustain. 6, 745–758. doi:10.1038/s41893-023-01147-z

CrossRef Full Text | Google Scholar

The Communist Party of China Central Committee and the State Council. (2021). Opinions on fully, accurately and comprehensively implementing the new development concept to Do a good job in carbon peaking and carbon neutrality.

Google Scholar

The State Council (2021). Carbon peaking action plan before 2030. (Guo Fa (2021) No. 23). Beijing, China: State Council of the People’s Republic of China.

Google Scholar

Wang, Y., Zhao, D., and Li, H. (2022). Evolutionary game analysis of carbon emission reduction between airlines and government under cap-and-trade mechanism. J. Air. Transp. Manag. 102, 102240. doi:10.1016/j.jairtraman.2022.102240

CrossRef Full Text | Google Scholar

Wei, Y. M., and Liao, H. (2021). Paths for China's energy low-carbon transition under the goals of carbon peaking and carbon neutrality. Chin. J. Popul. Resour. Environ. 31, 1–9. doi:10.1016/j.cjpre.2021.12.028

CrossRef Full Text | Google Scholar

Winchester, N., Malina, R., Staples, M. D., and Barrett, S. R. H. (2015). The impact of advanced biofuels on aviation emissions and operations in the U.S. Energy Econ. 49, 482–491. doi:10.1016/j.eneco.2015.03.013

CrossRef Full Text | Google Scholar

Yang, P. F. (2025). Green development strategies for China's sustainable aviation fuel industry. Petrol. petrochem. Green Low-Carbon 10, 1–4.

Google Scholar

Zhang, X. H. (2024). Analysis on the economy and development potential of sustainable aviation fuel (SAF). IOP Conf. Ser: Earth Environ. Sci. 1329, 012014. doi:10.1088/1755-1315/1329/1/012014

CrossRef Full Text | Google Scholar

Zhang, P., Ding, R., Wang, Y., Zhang, Q., and Zhao, L. (2023). Emission reduction with hybrid mechanisms in civil aviation: an evolutionary game approach. Front. Environ. Sci. 11, 1138931. doi:10.3389/fenvs.2023.1138931

CrossRef Full Text | Google Scholar

Zhang, W., Li, C., Jia, T., Wang, S., Hao, Q., and Yang, J. (2025). Evolutionary game analysis of sustainable aviation fuel promotion. Energy 322, 135723. doi:10.1016/j.energy.2025.135723

CrossRef Full Text | Google Scholar