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论文编号：	15911
作者编号：	2320234194
上传时间：	2025/12/12 21:01:57
中文题目：	L县木材加工业税收征管优化研究
英文题目：	Research on the Optimization of Tax Collection and Administration in the Wood Processing Industry in County L
指导老师：	张晓农
中文关键字：	木材加工业；税收征管；熵值法
英文关键字：	Wood Processing Industry；Tax Collection and Administration；Entropy Weight Method
中文摘要：	L县木材加工业是县域特色产业，2015年至2024年该行业税收收入平均占全县总税收收入的13.32%，其发展稳定性直接关联地方财政收支平衡与经济活力。但近年来，受行业特性与征管环境影响，该领域税收风险问题日益凸显，虚开案件频发。此类问题不仅导致大量税款流失，更对税收征管体系形成挑战，若不及时破解，将进一步扰乱行业税收秩序，削弱产业对县域经济的支撑效能。 本文以L县木材加工业税收征管为研究对象，聚焦县域特色产业税收治理中的现实问题，旨在探索符合行业发展特点的税收征管优化方案。首先通过文献研究法、案例分析法及数据分析法，系统梳理了国内外有关文献与木材加工业、税收征管等核心概念与理论。同时深入分析了L县木材加工业的产业规模、经营模式及现行税收征管框架，揭示了征管过程中存在的四大核心问题：一是数据质量待提升；二是管理方式落后；三是风险识别低效；四是风险应对质量不高。 在此基础上，本文提出针对性优化方案：一是改进数据采集来源，夯实内外部数据采集与运用；二是实行分级分类管理，优化企业等级评定并实行全流程监管；三是构建税收风险识别模型，利用皮尔逊相关分析法从23个初始指标中筛选出15个核心指标，并采用熵值法计算指标权重，结合3σ准则设计量化评分机制，并选取2022至2023年327户锯材加工一般纳税人作为样本，并以30户已被稽查定性虚开的企业为验证对象，证明模型预警准确率相较原有的预警方法大幅提升，且跨地区应用依然有效，证明该模型具备一定的实践价值；四是优化风险应对管理，制定差异化应对措施。 本文研究不仅丰富了特定行业税收征管理论，为县域木材加工业税收征管提供了可操作的优化路径，也能助力L县提升税收征管效率、促进产业健康发展及增加地方财政收入，同时为其他县域特色产业税收治理提供借鉴。
英文摘要：	The wood processing industry in L County is a county-level characteristic industry. From 2015 to 2024, the tax revenue of this industry accounted for an average of 13.32% of the county's total tax revenue, and the stability of its development is directly related to the balance of local fiscal revenue and expenditure as well as economic vitality. However, in recent years, affected by the industry's characteristics and the tax collection environment, tax risk issues in this field have become increasingly prominent, with frequent cases of false invoice issuance. Such problems not only result in the loss of a large amount of tax revenue but also pose challenges to the tax collection and administration system. If not addressed promptly, they will further disrupt the industry's tax order and weaken the industry's supporting role in the county's economy. This study takes the tax collection and administration of the wood processing industry in L County as the research object, focuses on the practical problems in the tax governance of county-level characteristic industries, and aims to explore an optimized tax collection and administration plan that conforms to the development characteristics of the industry. Firstly, through the literature research method, case analysis method, and data analysis method, this study systematically sorts out domestic and foreign relevant literature as well as core concepts and theories related to the wood processing industry and tax collection and administration. On this basis, it conducts an in-depth analysis of the industrial scale, business model, and current tax collection and administration framework of the wood processing industry in L County, and identifies four core problems existing in the tax collection and administration process: first, the data quality needs to be improved; second, the management method is backward; third, the risk identification is inefficient; fourth, the quality of risk response is not high. Based on the above analysis, this study proposes targeted optimization plans: first, improve the sources of data collection and consolidate the collection and application of internal and external data; second, implement hierarchical and classified management, optimize the enterprise rating system, and conduct full-process supervisionthird, construct a tax risk identification model: 15 core indicators are screened from 23 initial indicators using Pearson correlation analysis, the weights of the indicators are calculated by the entropy weight method, and a quantitative scoring mechanism is designed in combination with the 3σ criterion. Using a sample of 327 general taxpayers in the sawn timber processing industry from 2022 to 2023, and validating the model with 30 enterprises previously identified by tax inspection as involved in fraudulent invoicing, the results demonstrate a significant improvement in early warning accuracy compared to traditional methods. Furthermore, its application in other regions confirms sustained effectiveness, underscoring the model's practical value; fourth, optimize the risk response management and formulate differentiated response measures. This study not only enriches the theory of tax collection and administration for specific industries and provides an operable optimization path for the tax collection and administration of the county-level wood processing industry but also helps L County improve the efficiency of tax collection and administration, promote the healthy development of the industry, and increase local fiscal revenue. Meanwhile, it provides a reference for the tax governance of other county-level characteristic industries.
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