Tripartite Model Research Articles

Risk-averse behavior and incentive policies: A new perspective on spatial–temporal traceability supervision in construction logistics supply chains

Spatial-temporal decision support systems (STDSS) serve as a crucial strategy for enhancing operational governance and mitigating risk within construction logistics. However, construction supply chains involve many opaque stakeholders, hindering whole industry compliance monitoring. This research formulates a tripartite evolutionary game model that scrutinizes the strategic interactions among government regulators, carriers, and contractors, thereby offering insights into the collaborative supervision outcomes shaped by these stakeholder engagements. Government regulators choose intelligent supervision incentives versus regular oversight. Carriers decide whether to invest in STDSS or not. Contractors cooperate by enrolling or declining STDSS. As key STDSS investors, carriers’ decisions are investigated under varying risk preferences in an extension model. This examines how government incentives influence long-term intelligent supervision and risk aversion behavior emergence to improve safety and quality across construction supply chains. Our findings indicate that increased adverse events motivate government regulators to adopt STDSS incentives for oversight, though carriers and contractors are not necessarily prompted to implement STDSS themselves. Escalating risk aversion reduces carrier STDSS adoption likelihood as they maintain basic services. Carriers perceiving contractor free riding as unfair competition also demotivates STDSS rollout. Although larger subsidies initially raise STDSS implementation probability, carriers become unwilling to adopt STDSS over time even with greater subsidies. In summary, adverse events drive regulator but not necessarily carrier and contractor STDSS adoption, while risk aversion, perceived fairness and changing subsidy effectiveness over time shape carrier decisions.

Evolutionary game analysis of data sharing among large and medium-sized enterprises in the perspective of platform empowerment

With the swift advancement of the global digital economy, data has emerged as a critical component in fostering the integration of large enterprises with small and medium-sized enterprises (SMEs). Nevertheless, due to disparities in resources and capabilities between these entities, there is a deficiency in the willingness to share data, hindering the full actualization of data’s potential value. Hence, it is imperative to facilitate a novel cooperative development paradigm wherein platforms enable data sharing among large enterprises and SMEs. In this paper, we construct a tripartite evolutionary game model encompassing large enterprises, SMEs, and digital platforms, examine the evolutionary stable strategies adopted by these entities in the data sharing, and use numerical simulation to explore the system’s evolutionary stability under various influencing factors. Contrasting with prior research, this study considers the heterogeneity of enterprise scale and delves into the data sharing dynamics between large enterprises and SMEs. Simultaneously, it positions the digital platform as a player in the game, examining its impact on data sharing among the enterprises. Findings indicate that: (1) SMEs exhibit greater eagerness for data sharing compared to large enterprises, which display a U-shaped influence during the process; (2) Digital platforms are particularly sensitive to costs, with the platform’s initiative and the service quality will affect enterprises strategic choices; (3) Government subsidies positively encourage tripartite cooperation, and robust data security governance framework is crucial for enterprises. Finally, based on the results of the study and combining with the current situation of digital economy development, it puts forward the suggestions for promoting platforms to empower large enterprises and SMEs to realize data sharing and the prospects for future research.

The future of sustainable farming: An evolutionary game framework for the promotion of agricultural green production technologies

In the context of global climate change and increasing population, transitioning to resource-efficient, environmentally friendly agricultural technologies is essential for sustainability. In this context, the present study is anchored in the theories of information asymmetry and bounded rationality. It constructs a tripartite evolutionary game model under market mechanisms and government guidance, facing the challenges of promoting green agricultural technologies among multiple actors. This model explores the strategic choices and dynamic evolution of these actors under economic incentives and risk perception. The objective of the research is to reveal the dynamic evolutionary trends of stakeholder behavior under different policy intervention conditions and how governments can propel the promotion of green agricultural production technologies through policy instruments. To encompass the impact of historical decisions on the evolutionary process of green technology dissemination, this study employs a Delay Differential Equations (DDE) algorithm that considers historical time lags in its numerical simulations. Through stability analysis and DDE numerical simulations, the research identifies factors influencing the promotion of green agricultural production technologies. The findings indicate that under market mechanisms, there is a risk of “market failure” in the dissemination model of green agricultural technologies. Government participation can provide external incentive support, effectively enhancing the stability and efficiency of the evolutionary game system for promoting green technologies. Simulation results further suggest that government intervention can significantly improve the stability and efficiency of technology promotion. Economies of scale, initial willingness, risk perception, and subsidy intensity are decisive factors in technology adoption. Consequently, policymakers are advised to formulate precise and multifaceted subsidy strategies based on the varying needs of different actors, to stimulate engagement, reduce risk, and encourage widespread adoption of green technologies. This study lays a theoretical groundwork for smallholders and family farms to overcome technology adoption barriers, crucial for sustainable agricultural development. It provides a framework for understanding and addressing these challenges, guiding policymakers in crafting effective green agriculture policies to improve farming efficiency and sustainability.

Behavioral dedication, constraint or obligation? A tripartite model of active participation in multiplayer online battle arena game community

PurposeActive participation is critical to the survival and development of the multiplayer online battle arena (MOBA) game community. However, this issue has not received much attention in the information systems literature. To address this issue, we develop a tripartite model that accounts for the roles of behavioral dedication, constraint, obligation mechanisms on active participation in the MOBA community.Design/methodology/approachThe research model is empirically validated by online survey data among 971 users of a popular MOBA community.FindingsThe results show that perceived enjoyment, perceived escapism, and affective commitment are key behavioral dedication factors, which further promote active participation in the MOBA community. In addition, past investment, self-efficacy for change, and calculative commitment are important behavioral constraint factors, which ultimately influence active participation in the MOBA community. Finally, subjective norm, group norm, social identity, and normative commitment are influential behavioral obligation factors, which in turn facilitate active participation in the MOBA community.Originality/valueThis study contributes to the theoretical understanding of active participation in the MOBA community and offers practical guidance for promoting active participation in the community.

The optimization of school-university resources in an effort to recruit teachers of mathematics

PurposeThe purpose of this article is to debut a novel initiative that could potentially optimize resources that are currently constrained but, if unleashed, could help ameliorate the science, technology, engineering and mathematics teacher shortage. The initiative involves the reconceptualization of the National Network for Educational Renewal (NNER) tripartite model, which evolved from the work of Goodlad (1994a) and promotes cooperation and partnerships between the three important players responsible for preparing succeeding generations of competent teachers: PK-12 schools, university colleges of education and university colleges of arts and sciences (Roselle, Hands, Marino, Kilgallen, & Howard, 2021; Goodlad, 1994a).Design/methodology/approachThe approach used in writing the article was narrative, offering a brief review of the various challenges that have had an impact on the national teacher shortage, particularly in the field of mathematics.FindingsThe study suggests that a reconceptualized NNER tripartite model can be implemented to assuage the devastating effects of the coronavirus disease 2019 pandemic on learning, offer support to an overworked teacher workforce and provide a possible math teacher recruitment pipeline by forming a life-giving partnership between a college of education, a college of arts and sciences, a local elementary school and undergraduate math club members.Originality/valueThis is an original application of the NNER tripartite model, particularly with math teacher recruitment in mind, and it is hoped that the model will be considered transferable to a variety of school-university contexts. However, additional study is required to explore the validity and replicability of this model.

An evolutionary game and system dynamics approach for the production and consumption of carbon-labeled products-based on a media monitoring perspective

Carbon label is acknowledged as an effective way to combat the problem of global warming. As a powerful way to encourage individuals to adjust their consumption patterns and to promote the development of green consumption, carbon-labeled products are widely developed in China. To reveal the production and consumption process of carbon-labeled products, the present study constructs a tripartite game model consisting of the government, carbon-labeled products manufacturers and consumers based on a media monitoring perspective. The evolutionary stability strategy (ESS) is firstly determined by solving the replication dynamic equations and stability analysis of the equilibrium point, followed by the strategy analysis and sensitivity analysis through numerical simulation. The results show that media supervision can effectively complement and constrain government supervision. In addition, it can promote enterprises to standardize production and enhance consumers' trust and willingness to buy carbon-labeled products. The introduction of media supervision can well realize the ideal equilibrium of "effective government supervision, enterprise compliance and consumer support for purchase".

Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

Carbon emissions have become particularly urgent with the acceleration of industrialization. Currently, AI-based carbon emission modeling provides essential support for solving the problem of predicting, managing, and optimizing carbon emissions. However, there are still challenges in accurately predicting carbon dioxide emissions and establishing an appropriate carbon emission regulatory model to manage carbon dioxide emissions effectively. Firstly, this paper introduces a framework for learning seasonal and trend representations called DeTF (Decoupling representation for Time and Frequency domain of time series). The framework is designed for time series data of CO2 emissions used for power generation from 1973 to 2023. Secondly, we use various data enhancement techniques to improve the robustness of the learned representations. Finally, the prediction results establish a tripartite evolutionary game relationship between the state, regulatory agencies, and enterprises. We use MSE and MAE as evaluation metrics, and our model overall outperforms current state-of-the-art end-to-end algorithms. It outperforms the best performing end-to-end prediction method by 27.08% (MSE) in the prediction results. Besides, the time series prediction results are innovatively combined with the tripartite game model to analyze the main factors determining the optimal strategy.

Conceptualizing digital placemaking in nature for wellbeing

Increased urban population has created a disconnection between humans and natural environments that needs to be recognised as a key challenge. This article proposes that disconnection from nature in urban settings can be mitigated by digital placemaking. A conceptual framework which accounts for place attachment and place branding, Social Identity Theory and nature connectedness is presented. The benefit of this approach is that it can help us understand more clearly the different dynamics involved in hybrid place experience as a driver for both social change and consumer wellbeing. This modified hybrid tripartite model creates meaningful places for consumers, fostering their attachment to the space and with others, while simultaneously enhancing their wellbeing. In addition, the model contributes to our understanding of place branding effects on consumers beyond commercial outcomes; explaining the positive effects on wellbeing of nature-based digital placemaking; and providing clarity on the role of technology in nature-based place brand experiences. In synthesising the different dimensions of the framework, potential implications for place managers, marketers and leaders are outlined.

An evolutionary game-theoretic approach to analysis the green innovation chain dynamics under government policies

Continuous improvement of the green innovation chain is essential for promoting sustainable entrepreneurship and addressing environmental issues. In this paper, we developed a tripartite evolutionary game model involving tech firms, startups, and accelerators. Our goal is to examine the impact of both static and dynamic government interventions on the evolution of the green innovation chain. After determining the evolutionary stable strategies, we examine government interventions through a system dynamics simulation using three different scenarios: (1) Government support downstream of the innovation chain, (2) government support upstream of the innovation chain, and (3) government support throughout the chain. In the first scenario, it is observed that although it promotes green technology innovation (GTI), the necessary support for startups and accelerators to scale up green innovations is lacking. Consequently, the overall level of GTI is limited. Conversely, in the second scenario, there is a lack of incentives for tech firms to invest in GTI, resulting in a stagnation in the innovation chain. Nevertheless, with government support extended throughout the chain, a synergistic effect emerges between all players, leading to a higher level of collaboration and resulting in a more efficient and effective green innovation chain.

Research on Strategy Optimization of Green Agricultural Production Trusteeship to Promote Black Land Protection

The whole trusteeship of green agricultural production plays an important role in promoting the protection of black land, and it is particularly crucial to clarify the behavioral characteristics and game relationships of agents involved in the whole trusteeship. This study uses the dynamic evolutionary game method to construct a tripartite evolutionary game model of governments, service organizations and farmers participating in the whole trusteeship of green agricultural production, aiming to come up with effective strategies to promote the widespread application of green agricultural production trusteeship and achieve agricultural green development. The results are as follows: (1) At present, the agricultural production model in Northeast China is dominated by traditional agricultural production and supplemented by green agricultural production. (2) Incentive and punitive measures will encourage agents to adopt positive strategies. (3) In areas with a poor awareness of green agricultural production trusteeship, the government’s incentive and punishment measures for farmers and service organizations are imbalanced. (4) The relatively high cost of trusteeship leads to a lack of market competitiveness, which has a negative impact on service organizations promoting green agricultural production trusteeship. This study provides an effective reference for improving the overall implementation effect of black land protection in Northeast China.

Evolutionary game analysis on cross-border cooperative R&D of intelligent new energy vehicle industry

PurposeIntelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.Design/methodology/approachThe authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.FindingsResults showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.Research limitations/implicationsThe research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.Originality/valueThe INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.

Economic psychology for laymen

Neuroeconomics can now replace the Paradigm of Bounded Rationality (BR) by a rooting in positivist economic psychology. The key finding is that the neuroeconomic Model of Decision-Making (NeM), based on neuroeconomic trials, suspends subjectivism, identifying risk-willingness as a basal parameter of economic behavior. Further, NeM and behavioral psychology (Big5 Taxonomy) integrates correlation studies into an Economic Psychology that simplifies Sensi-training in the Big5 to persons without specialism in Psychology. Finally, this Economic Psychology implicates a new interdisciplinary outlook on three domains of Economics: Evidence of the Florida Thesis on the Rise of the Creative Class as a qualitative growth effect A positivist framework for modern stress-management by classical meditative in-depth relaxation Recognition of prejudices as a special problem of behavioral science due to the interaction of subject and object Discussion focuses on the significance to economic growth theory to abandon BR. The conclusion is, that the simple model of accounting real growth in GDP, must be replaced by a tripartite model already termed 3P in management research (Planet, Personnel and Profit) including negative growth factors, too. The 3P has a common outcome in Quality-Adjusted Life Years (QALY) which at the global level has a negative aggregated value in the 21 Century. So, political measures to counterbalance, for instance the damage to the climate, are discussed concluding an urgent need for a global CO2 Tariff (ET).

Evolutionary game and system dynamics for analysis on stakeholder strategies of regional high-speed rail project in investment decision stage

This paper studies the strategic choices under the conflict among stakeholders of regional high-speed rail project at the investment decision stage in the context of the ‘high-speed rail controversy’. This study develops a tripartite evolutionary game model among the central government, the local government and China Railway, analyzes the evolutionary stability strategy, discusses the influence of multiple factors on the choice of tripartite strategy, and further calculates and analyzes the stability of the equilibrium point in the tripartite evolutionary game system. Then the dynamic change trend of strategy choice brought by the change of each factor is investigated through system dynamics simulation. The results show that the lower the intervention cost and reward to the other two parties, and the increase of the opportunity cost of non-intervention and punishment to the other two parties, the faster the convergence of the central government's choice of intervention strategy. The increase of the rewards and punishments, and the increase of negotiation profits, non-negotiation costs, and loss of reputation for non-negotiation, and the decrease in the negotiation costs and non-negotiation profits all contribute to the shortening of the convergence time of the local government's choice of negotiation strategy. The increase of the rewards and punishments, the increase of support profits, the loss of reputation for non-support, and the decrease of the profits for non-support and the extra cost for support will promote the convergence of China Railway's choice of the support strategy. Finally, the simulation results are discussed so as to target feasible coordination strategies, which provides a theory basis for the practice of conflict coordination among stakeholders of regional high-speed rail project at the investment decision stage.

Multi‐agent behavioral strategy game and synergy evolution path of the retired power battery recycling system

AbstractIn the era of transitioning to a low‐carbon economy, China's electric vehicle market is experiencing exponential growth, a trend that is mirrored by the burgeoning market for the recycling of used power batteries. This rapid expansion underscores the urgency of enhancing the power battery recycling system. However, the lack of sufficient engagement from consumers and recyclers in the recycling system currently impedes the formation of a cohesive recycling effort. To tackle this issue, we introduce a tripartite evolutionary game model involving the government, battery recyclers, and consumers. By analyzing the evolutionary stability strategies (ESSs) and their stability conditions, we delineate the synergetic development paths of them. Numerical examples illustrate the model's effectiveness and explore how government incentive policies can affect the strategic decisions of other participants. Our findings suggest that establishing incentive policies by the government in the early stage of the power battery industry is crucial for fostering a synergetic recycling system that is led by the government, involved by the community, and driven by the market.

Efficient distributed association management method of data, model, and knowledge for digital twin railway

ABSTRACT Digital twin railway is a pivotal foundation for the intelligent construction and maintenance of railway engineering projects within extensive open spaces. Its essence is the integrated representation and association management of multi-granularity spatiotemporal data, executable analysis models, and professional knowledge. These elements are characterized by the prominent characteristics of multi-source, heterogeneity, and massive volume. However, current decentralized and independent management strategies often neglect the dynamic coupling relationships between them, and numerous multi-path joins and conversion aggregation operations exist across various spatial scale applications. Consequently, this results in challenges such as the inability to dynamically couple data-model-knowledge and conduct global association retrieval, thereby limiting the potential for real-time analysis and intelligent application capabilities. To address these problems, we first constructed a tripartite graph model ( DMK − Graph model ) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ( DMK − Graph index ) and a global-local indexing mechanism. Finally, a prototype system for railway data-model-knowledge association management was developed. The effectiveness of the distributed association management method was demonstrated by employing a case study of high-temperature safety risk analysis in railway tunnel engineering with multi-physics field coupling.

The impact of penalties, incentives, and monitoring costs on the stakeholders’ decision-making behaviors in non-compliance drone operations

As an automated assistive tool, drones have revolutionized industrial activities and brought numerous potential benefits to society. However, irresponsible drone users often disregard compliance with regulations, leading to new challenges in drone usage. Although governments have implemented punishment and incentive mechanisms to prevent non-compliant drone operations, the extent to which they can effectively deter such activities remains unclear. To address this gap, the study employed evolutionary game theory to assess the impacts of penalties for non-compliance, incentives for public monitoring, and monitoring costs for the government on the multiple stakeholders' decision-making behaviors (SDBs). The study also used the Chinese construction market data to simulate how penalties, incentives, and monitoring costs influence SDBs. The numerical simulations reveal that penalties and incentives could reduce drone users' non-compliant operations, but this effect is useful only if the penalties and incentives exceed a certain value. In China, drone users' non-compliant operations can be controlled when penalties for drone users exceed 12,000 yuan, and incentives for the public's monitoring exceed 170 yuan/day. The current Chinese government's penalties that were administered for non-compliant drone operations have not achieved a deterrent effect, but the incentive is feasible. These findings provide a fresh insight into the decision-making behaviors of stakeholders in non-compliant drone operations. Additionally, the tripartite evolutionary game model developed in this study can assist other countries in determining optimal values for penalties, incentives, and monitoring costs to mitigate non-compliant drone operations effectively.

Evolutionary Tripartite Game Model in the Coal Mine Risk-Hazard Double Pre-Control and Management in China

The classification, investigation, and management of the safety risk (namely, risk-hazard double pre-control management mechanism) in coal mines were introduced into a behavioral evolution game to assess the decision-making processes of coal mine workers, regulators, and supervisors. A tripartite game tree and a revenue perception matrix were constructed, and the evolutionary stability strategy (ESS) achieved by the evolutionary system under different conditions was obtained according to the replication dynamic equation (RD). The numerical simulation of the evolution game was conducted in MATLAB, which verified the influence of various parameters on the behaviors adopted by all parties. The results show: (1) When the safety, regulatory, and supervisory costs are high, the average expected value of mine workers, regulators, and supervisors to choose safety behaviors is low, so they adopt an unsafe behavior. By contrast, reducing costs can encourage the adoption of safety behaviors. (2) When the punishment for accident losses caused by unsafe behavior and the lack of regulations and supervision is higher than 50% of the initial state, the actors in the tripartite game are more inclined to avoid risks when facing cost losses in the short term, which can lead them to choose a safety behavior. (3) When the actors in the tripartite game face different accident probabilities and accident loss, with the severity of the accident increasing to 80% of the initial state, mine workers and regulators gradually adopt safety and regulation behaviors, and supervisors quickly adopt supervision methods.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst ‘Big Data’, prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with ‘Rain.lta’, reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with ‘NDVI.lta’, reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.

Deciphering Stakeholder Strategies in Electric Vehicle Battery Recycling: Insights from a Tripartite Evolutionary Game and System Dynamics

Given that the global fleet of electric vehicles is expanding rapidly, leading to a surge in demand for power batteries, the significance of battery recycling has come sharply into focus. In this paper, we develop a tripartite evolutionary game model coupled with a system dynamics simulation approach to analyze the behavioral mechanisms of stakeholders in electric vehicle battery recycling and to identify the key factors influencing their strategic transformations. The model involves three key stakeholders: local governments, battery cathode manufacturers, and battery pack manufacturers. Through profit analysis and the formulation of a replicator dynamics system, coupled with the application of Vensim for system dynamics modeling, we analyze the effects of ten crucial exogenous variables on stakeholder strategies. Our research highlights the crucial role of government actions, such as subsidies and penalties, in directing corporate strategies. Notably, excessively high subsidies (over 2.1 million Chinese Yuan) may lead to dependency among cathode manufacturers. Market factors, including increased battery sales and the preference for low-carbon procurement by electric vehicle manufacturers, are shown to significantly motivate cathode manufacturers to adopt recycling strategies and encourage pack manufacturers to develop recyclable designs. Additionally, the introduction of EU battery regulations and the integration of cathode manufacturers into carbon trading schemes have been identified as positive catalysts for strategic shifts, especially for cathode manufacturers, while exerting minimal influence on pack manufacturers. This paper elucidates the intricate interaction of the behavioral patterns and key motivators affecting stakeholder strategies in electric vehicle battery recycling.

Mineral resource drivers in the global south: A case study of Australia

Mineral resource depletion directly restricts sustainable mineral development. This study investigates the drivers of sustainable mineral resource development via an evolutionary game model involving mineral firms, the Australian government, and financial institutions. The tripartite model provides a guide to strategies and interactions with external influences based on company liquidity. Leveraging panel data from Australian mining companies from 2015 to 2022, this study utilizes unconditional quantile regression techniques, culminating in three salient discoveries. First, despite financial limitations, small-to medium-sized enterprises (SMEs) can accumulate ample resources to enhance their green technology R&D and corporate social responsibility (CSR) expenditure through diverse financing methods, thereby creating an internal strategic impetus for CSR perpetuation. Second, by participating in CSR, mineral firms can transmit positive signals in an asymmetric market, which promotes their sustainable development. Finally, despite the Australian government's R&D tax refund policy incentivizing R&D investment, SMEs' lack of innovative capacity has led to the diversion of operational funds, which negatively impacts their sustainable development. These findings provide valuable insights for sustainable mineral resource development that extend beyond Australia to other Southern Hemisphere nations.