Recherche sur le comportement de production et la régulation collaborative des entreprises d’habillement durable sous les contraintes des émissions de carbone et les incitations gouvernementales
Year: 2025
Type: dissertation
Abstract: Against the backdrop of intensifying global climate change and the widespread implementation of carbon reduction strategies, traditional high-energy, high-pollution industries are facing profound transformation pressures. The apparel industry significantly impacts the environment through carbon emissions, water consumption, and chemical pollution. Its transformation has thus become a vital component of the global sustainable development agenda. However, the industry's transition is hindered by multiple challenges, including uncertain corporate production decisions, divergent consumer willingness to pay, limited effectiveness of policy incentives, and the absence of regulatory oversight on e-commerce platforms. Therefore, systematic research is urgently needed to provide scientific foundations and effective support for decision-making and policy formulation during the industry's transformation.This study focuses on the apparel industry, examining key factors such as carbon emission costs, government subsidy mechanisms, consumer environmental awareness, and e-commerce platform governance. It centers on four core themes: enterprise production decisions, carbon price forecasting, policy incentive effects, and platform governance. By integrating machine learning algorithms, game-theoretic approaches, and numerical simulation techniques, this research constructs a multi-level analytical framework comprising a single-firm production decision model, an interval carbon price prediction model, a duopoly competition model, and a tripartite evolutionary game model. This framework systematically characterizes the strategic interactions and response mechanisms among enterprises, consumers, platforms, and governments, aiming to provide scientific pathways and policy recommendations for a green and low-carbon transition in the apparel industry.First, based on heterogeneous consumer preferences, this study develops a production decision model for firms facing the choice between traditional and sustainable products. The model reveals the optimal strategic behaviors of enterprises under the influence of environmental awareness, carbon emission costs, and consumer willingness to pay.Second, this study proposes an interval carbon price forecasting framework that integrates Extreme Gradient Boosting (XGBoost) feature selection, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization algorithm, and a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model. This approach overcomes the limitations of traditional point forecasting models in dealing with uncertainty and significantly improves forecasting accuracy and stability.Third, a duopoly competition model is constructed to comparatively analyze the impacts of government subsidies targeting firms versus consumers on market structure, product pricing, corporate profits, and social welfare. The findings indicate that subsidies directed at firms are more suitable for the early stages of transition—when technology is immature and transformation costs are high—as they effectively stimulate R&D and green technology adoption.Finally, this study develops a tripartite evolutionary game model involving producers, consumers, and e-commerce platforms to explore the formation mechanism and regulatory dilemmas of pseudo-sustainable practices on digital platforms. Results show that firms' decisions to engage in sustainable production are jointly influenced by regulatory intensity and cost constraints; consumer reporting behavior is affected by incentive structures and response mechanisms; and platform regulatory strategies involve a dynamic trade-off between reputational gains and regulatory costs.
Source: theses.fr (ABES)
Author: Sha, Di
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7142578587
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