Carbon Reduction Targets Research Articles

Can Tourists’ Summer Vacations Save Energy and Reduce CO2 Emissions? Evidence from China

This study develops a methodological framework for measuring energy conservation and CO2 emission reductions that considers both origins and destinations. The framework encompasses four key aspects: transportation, accommodation, cooking, and housing rehabilitation. Data were collected through a literature review, questionnaire surveys, and field measurement tracking. Compared to living in the origin, senior tourists from Nanchang visiting Zhongyuan Township in China for summer tourism can save 5.747 MJ of energy and reduce CO2 emissions by 3.303 kg per capita per day. An in-depth analysis indicated that the research site could further enhance energy conservation and reduce CO2 emissions by improving public transportation services, optimizing the energy structure of the destination, and diversifying the available recreational offerings. Depending on the characteristics of the destination and the primary origin, summer or winter tourism in various countries or regions can employ the methodological framework to evaluate energy conservation and CO2 emission reductions after identifying specific parameters. The improved pathways identified through this research can serve as a checklist for other countries or regions aiming to explore energy conservation and CO2-emission-reduction pathways for summer or winter tourism. Enhancing climate-driven tourism development may offer a new avenue for the tourism industry to contribute to carbon reduction targets.

Advanced Emission Reduction Strategies: Integrating SSSC and Carbon Trading in Power Systems

The global power sector faces the critical challenge of balancing rising electricity demand with stringent carbon reduction targets. Taiwan’s unique geopolitical and energy import constraints provide an ideal context for exploring advanced grid technologies integrated with carbon-trading mechanisms. This study combines the Adaptive Time-Varying Gravitational Search Algorithm (ATGA) with Static Synchronous Series Compensator (SSSC) technology to optimize power flow and enable carbon transactions between the power generation and transmission sectors. Through a feedback-driven mechanism, power producers acquire carbon credits from transmission operators, maximizing profitability while meeting emission targets. Managed by the transmission companies, the SSSC enhances grid stability, reduces transmission losses, and generates valuable carbon credits. Simulations based on Taiwan’s power market demonstrate that this integrated approach achieves a 50% reduction in emissions and increases profitability for power producers by up to 20%. This model has potential applications in other regions, and future work could explore its scalability and adaptability in different economic and regulatory contexts.

Analysis of Energy-Related-CO2-Emission Decoupling from Economic Expansion and CO2 Drivers: The Tianjin Experience in China

Cities are key areas for carbon control and reduction. The study of the decoupling between CO2 emissions and gross domestic product (GDP) and the drivers of CO2 emissions in cities facilitates the reduction of CO2 emissions to safeguard the development of the economy. This paper first calculates the CO2 emissions in Tianjin, China, from 2005 to 2022, then uses the Tapio decoupling index to quantify the decoupling status, and, finally, explores the energy-CO2-emission drivers through the Logarithmic Mean Divisia Index (LMDI) model. The findings indicate that (1) the decrease in CO2 emissions from industrial products and transport is the main reason for the decline. (2) During the period under investigation, the predominant condition observed was a state of weak decoupling. (3) Given the economic-output effect is the primary and substantial driver of energy CO2 emissions, it is essential to harmonize the interplay between economic-development approach and CO2 emissions to foster sustainable development in Tianjin. The industrial structure plays the most critical role in hindering the reduction of CO2 emissions; therefore, optimizing industrial structure can help achieve carbon reduction and control targets. These findings enrich the study of CO2 emission factors and can also interest urban policymakers.

A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs

As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.

Carbon Peak Prediction and Carbon Reduction Path Simulation under the Dual Carbon Target: A Case Study of Jiangsu Province

To build a low-carbon and harmonious society that supports sustainable development, China has set forth the significant strategic goal of achieving carbon peaking by 2030 and carbon neutrality by 2060, commonly referred to as the dual carbon target. The key to achieving this target lies in scientifically and accurately predicting the timeline for carbon peaking, followed by developing effective carbon reduction paths. Using Jiangsu Province as an example, this study utilizes energy and economic data from 2010 to 2020 and introduces a penalized Lasso-Kaya model to forecast carbon emissions. The applicability of deep learning models, such as LSTM, in carbon peak prediction is also validated. Based on this, three levels of carbon reduction targets were established: baseline, steady progress, and ambitious. Through parameter simulation, the study derives an optimal carbon reduction path for Jiangsu Province through 2060. Consequently, this paper proposes a quantitative assurance mechanism for carbon reduction that maximizes economic growth, providing a valuable reference for future policy intervention.

Are Governmental Policies an Effective Way to Reduce Agricultural Carbon Emissions? An Empirical Study of Shandong in Main Grain Producing Areas of China

In the context of global and national carbon reduction targets, agricultural carbon emissions have become a critical focus. As global food demand increases, numerous agricultural policies have been implemented. Faced with limited policy resources, evaluating the impact of these policies on agricultural carbon emissions and production is essential. This study examined the relationship between food production and agricultural carbon emissions during the stage of agricultural development in Shandong Province, one of China’s major grain-producing regions, using the decoupling model. Additionally, the coupled coordination model was employed to assess the specific influence of agricultural policy clusters on this transformation. The results indicate that Shandong is transitioning from high-input, extensive farming to green, low-carbon, modern agriculture, with most cities shifting from strong negative decoupling to strong decoupling. Over time, the role of agricultural policies in driving this shift has grown more significant. Future policymaking should prioritize the overall quality of agricultural producers and maintain a continuous focus on sustainable, green development. Ensuring that policy directions align with evolving stages of agricultural development and adjusting them in real-time will be crucial.

Complexity and synchronization of carbon and new energy markets based on multiscale entropy

AbstractThis study provides a comprehensive analysis of the dynamic evolution of complexity and synchrony between the carbon market and the new energy market from 2019 to 2023, employing multiscale sample entropy and cross‐sample entropy methods. The study shows that, both in the long term and short term, the complexity of the new energy market consistently exceeds that of the carbon market. Additionally, the carbon and new energy markets exhibit higher synchronization on smaller scales. As the time scale increases, interactions between the carbon and new energy markets become more complex and diverse. According to these findings, it is recommended that policymakers improve the transparency of the new energy market, optimize the operational mechanisms of the carbon market, and ensure that carbon reduction strategies and new energy policies are mutually coordinated. This holds significant importance for both carbon reduction targets and the development of new energy.

Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.

A low-carbon supply chain pricing mechanism considering CSR under carbon cap-and-trade policy.

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare.

Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.

Development of efficient CO2 adsorbents: Synthesis and performance evaluation of dopamine-modified halloysite nanotubes loaded with ZIF-8

Carbon capture, utilization, and storage (CCUS) is a crucial technology for achieving carbon reduction targets. The key to CCUS technology lies in developing and designing efficient and low-cost CO2 capture materials. Metal-organic frameworks (MOFs) have emerged as promising CO2 adsorbents in the field of carbon capture. However, high agglomeration of nanoparticles and high cost have severely hindered the application of MOF-based solid adsorbents. This study employs an in-situ growth method to prepare a novel “corncob-shaped” PHNT@ZIF-8 composite material. During the reaction process, surface modification of halloysite nanotubes (HNT) enhances the interaction between HNT and ZIF-8. The resulting PHNT@ZIF-8 exhibits an enhanced CO2 adsorption capacity of 1.48 mmol/g. Notably, the functionalized PHNT@ZIF-8 as CO2 adsorbents demonstrated significant adsorption properties: a high specific surface area of 409.74 m2/g, good selectivity in simulated flue gas composition of 15%/85% (CO2/N2) (39.1), excellent thermal stability, and superior regenerability. Moreover, PHNT@ZIF-8 adsorbs CO2 primarily through van der Waals forces, which involve low energy and are easily reversible. After eight adsorption–desorption cycles, CO2 capacity retention rate remains as high as 93% (1.37 mmol/g). This work enriches the repertoire of solid CO2 composite adsorbents, providing a novel pathway for enhancing CO2 adsorption performance.

Comparative analysis of embodied carbon in modular and conventional construction methods in Hong Kong

Addressing the rise in global temperatures and the associated increase in greenhouse gases, particularly carbon dioxide, is a critical challenge necessitating innovative approaches within the building sector, a significant contributor to worldwide carbon emissions. While previous studies have demonstrated the prefabrication’s potential in reducing emissions, comprehensive assessments using actual project data for buildings constructed entirely with modular methods in Hong Kong are lacking. This study bridges this gap by evaluating the modular integrated construction (MiC) method through an embodied carbon assessment of the Kai Tak Community Isolation Facility. Using comprehensive project data from China State Construction (HK) Limited, the research conducts a comparative analysis between the actual emissions of the MiC method and those of a hypothetical conventional construction approach. Quantitative analysis reveals that MiC achieves a 20.7% reduction in embodied carbon, primarily due to shortened construction timelines, decreased waste generation, and optimized material usage. This significant reduction suggests substantial potential for decreasing the construction industry’s carbon footprint. The study provides empirical evidence supporting the environmental benefits of MiC in Hong Kong construction industry, promoting its broader adoption of MiC as a strategy for achieving carbon reduction targets. The findings align with Hong Kong’s carbon neutrality goals and contribute to the global initiative to mitigate the effects of climate change.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

The synergetic competition of cross-regional integrated energy systems with low-carbon technology heterogeneity under carbon emission trading mechanism to achieve carbon reduction target

The integration of renewable and fossil energy in a Region Integrated Energy System (RIES) is recognized as an effective measure for carbon emission reduction. The emergence of carbon emission trading (CET) markets worldwide has raised the need to combine RIES optimal planning with CET to achieve carbon reduction targets. This study focuses on the synergetic competition of a cross-RIES system with low-carbon technology heterogeneity under the CET mechanism. A cross-RIES model is constructed, incorporating fossil Carbon Capture System (fossil-CCS), renewable Combined Cooling Heating and Power (renewable-CCHP), and cleaner production paradigms. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to propose a bi-level multi-objective optimal planning framework for the cross-RIES model, employing a grandfather permit allocation method. Comparative analysis is conducted between cross-RIES optimal planning with and without considering the CET mechanism, with a focus on environmental indexes such as carbon emission, carbon intensity, and marginal abatement cost. Sensitivity studies are also performed to explore the impact of key CET design parameters on the system's environmental performance. The simulation results demonstrate that the CET mechanism effectively reduces carbon emission, primary energy consumption, and carbon intensity by promoting synergetic competition among the low-carbon technology heterogeneity of RIESs. Furthermore, the environmental performance varies among the three types of RIES. In terms of CET parameter design, lowering the cap constraint, reducing the allowance allocation ratio of fossil-CCS RIES, and increasing the trading price contribute significantly to reducing carbon emission and carbon intensity, albeit at the expense of higher marginal abatement cost.

Carbon emission prediction in a region of Hainan Province based on improved STIRPAT model.

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction.

An innovative analytical framework for energy consumption behavior: A study based on Beijing households

Residents’ energy consumption behavior has significant impacts on the achievement of carbon reduction targets and the effectiveness of related policies. Up to now, there has not been a complete framework that can accommodate internal psychological factors and external environmental factors. Based on the Planned Behavior theory and Value-Belief-Norm theory, this paper has added economic factors, policy impact, and convenience of consumption as important external environmental factors into a proposed model; in addition, knowledge level, behavioral expectations, and consumption habits are incorporated as new internal psychological indexes to construct a expanded framework. The framework integrates both internal psychological and external environmental factors, enriching and deepening the psychological foundation of behavioral analysis. After performing outlier detection, confirmatory factor analysis, and other steps on samples obtained from a questionnaire survey, the results of the framework fitting data show that it has high explanatory power for residents’ energy consumption behavior, which is significantly better than the existing models. Furthermore, the new critical path that determines Beijing residents’ energy consumption behavior is obtained by using the framework. In summary, this paper presents theoretical and empirical foundations for designing and enhancing low-carbon policies.

Shanghai Transport Carbon Emission Forecasting Study Based on CEEMD-IWOA-KELM Model

In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai’s transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai’s transport sector. First, nine indicators were screened as the influencing factors of Shanghai’s transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai’s transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets.

Transition finance facilitates lower-cost achievement of climate targets: A case study of China

Low-carbon transition in carbon-intensive industries is crucial for achieving climate targets. However, it's challenging for enterprises and projects in these industries to access the necessary funding for their transition within the existing financial system. In this context, transition finance supports the low-carbon transition of carbon-intensive industries, injecting momentum into achieving carbon neutrality goals. However, the precise impact of transition finance on the macroeconomy remains uncertain, and the effective strategies for its implementation to effectively advance national climate objectives are still unclear. This study integrates the stock-flow consistent method into a computable general equilibrium model to evaluate the macroeconomic impact of transition finance supporting China's climate goals. Results indicate that promoting investment in energy systems capital within carbon-intensive industries through transition finance can reduce the economic cost of achieving carbon reduction targets in China, with a reduction ranging from 0.02 % to 0.26 %. Additionally, transition finance optimizes the energy structure in the short term, mitigates the exit of high-carbon industries, and lessens the impact of climate targets on these enterprises. However, implementing transitional financing requires enhanced information disclosure and strict regulation of fund flows to mitigate potential credit risks. The research findings provide valuable insights into China's transition finance policy formulation and serve as a reference for other regions seeking financial support to achieve climate targets.

Land use policy implications of demographic shifts: Analyzing the impact of aging rural populations on agricultural carbon emissions in China

China is facing a unique demographic challenge with its population entering a phase of negative growth, alongside the growing issue of Rural Population Aging (RPA). This has brought significant attention to the broader population challenges the country is encountering. While the aging of rural populations has been a focal point of discussion, there remains a need for deeper insights into how it specifically impacts Agricultural Carbon Emissions (ACE). To address this knowledge gap, this study has devised a research framework grounded in the human-land relationships theory. This framework seeks to analyze the influence of RPA on ACE. Empirical tests have been carried out using provincial (municipal) panel data spanning from 2002 to 2019. The study's findings indicate that RPA exacerbates ACE, particularly in regions with a pronounced aging rural demographic. However, this adverse effect is lessened in coastal areas and placed with a lighter rural aging phenomenon. Additionally, the negative impact gradually diminishes in the long term. From a land utilization perspective, RPA contributes to Land Misallocation (LM), serving as a critical conduit through which RPA influences ACE. Nevertheless, mitigating factors such as Land Transfer (LT) and Land Scale (LS) help alleviate this negative influence. This mitigation is more pronounced in the long run. In the context of smallholder farmers, the study suggests that a moderate-scale expansion and the promotion of new agricultural models can be instrumental in achieving broader agricultural carbon reduction targets.

An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies

The utilization of conventional energy sources commonly leads to heightened energy consumption and the generation of specific forms of environmental pollution. As an innovative power management and dispatch system, virtual power plants (VPPs) have the potential to significantly enhance the flexibility and stability of power systems, while supporting carbon reduction targets by integrating distributed energy resources (DERs), energy management systems (EMSs), and energy storage systems (ESSs), which have attracted much attention in the power industry in recent years. Consequently, it can effectively address the variability and management challenges introduced by renewable energy. Furthermore, optimizing power market dispatch and user-side power management plays a pivotal role in promoting the transition of the energy industry towards sustainable development. The current study highlights the unresolved issue of strategic decision-making among market participants, such as energy companies, generation companies, and power distribution companies, despite the potentially significant benefits of VPPs. These entities must carefully evaluate the costs and benefits associated with adopting a VPP. Additionally, governments face the complex task of assessing the feasibility and effectiveness of providing subsidies to incentivize VPP adoption. Previous research has not adequately explored the long-term evolution of these decisions in a dynamic market environment, leading to a lack of adequate understanding of optimal strategies for market participants and regulators. This paper addresses this critical research gap by introducing an innovative bilateral evolutionary game model that integrates VPP and carbon trading markets. By utilizing the model, simulation experiments are carried out to compare different strategic decisions and analyze the stability and long-term evolution of these strategies. Research findings indicate that the adoption of VPP technology by market participants, in conjunction with government policies, results in an average 90% increase in market participants’ earnings, while government revenues see a 35% rise. This approach provides an alternative method for understanding the dynamic interactions between market participants and government policy, offering both theoretical and practical insights. The findings significantly contribute to the literature by proposing a robust framework for integrating VPPs into electricity markets, while offering valuable guidance to policymakers and market participants in developing effective strategies to support the sustainable energy transition. The application of this model has not only enhanced the understanding of market dynamics in theory, but also provided quantitative support for strategic decisions under different market conditions in practice.