近年来，随着全球对气候变化和可持续发展问题的日益重视，低碳经济发展与“双碳”目标的推进备受学界关注。“双碳”战略通过减排与增汇的双重路径，引领经济向绿色可持续方向转型。在此进程中，新能源汽车在能源革命与碳减排的领域发挥着核心作用。数据显示，2023年我国新能源汽车市场渗透率已达31.6%，并持续保持增长态势。然而，激烈的行业竞争使得众多新能源车企面临资金链脆弱、持续经营困难、盈利水平低下等财务困境。在此背景下，准确评估新能源车企财务风险不仅关乎企业自身的风险管理需求，更是投资者决策的重要依据。因此，新能源汽车企业亟需建立健全财务风险防控体系，通过风险预警与精准管控，切实提升企业风险抵御能力。

本文将根据新能源车企行业现状及赛力斯实际情况，识别赛力斯所面临的内外部财务风险，结合新能源车企的自身特点，从盈利、营运、偿债及发展风险四个维度筛选指标，建立适用于新能源车企的财务风险预警模型，对赛力斯的财务风险进行预警研究。首先，本文阐述了研究背景与研究意义，并简要概括了新能源行业财务风险研究现状和已有的财务预警模型，并对财务风险及财务风险预警等关键概念进行界定。其次，分析案例公司所处的行业环境及公司基本情况，利用“IE矩阵”模型，对其内外部因素进行识别并分析其财务风险。该模型识别出赛力斯面临的内部风险主要集中于：企业高额研发支出、较高的资产负债率和过度依赖单一市场；外部风险主要集中于原材料价格波动与供应链压力、全球贸易壁垒与政策不确定性。将识别出的风险，按照盈利风险、营运风险、偿债风险及发展风险四个方面进行归类。再次，利用层次分析法筛选出权重较高的财务风险指标，通过显著性分析、多元共线性检验和费希尔判别法等方法，在F分数模型的基础上，建立适用于新能源汽车行业的财务风险预警模型并优化其财务风险等级的判定区间。利用建立的财务风险预警模型，从横向和纵向的两个角度对其财务风险进行预警分析。最后，基于以上研究，从内外部风险防范的角度提出相关建议，以期为赛力斯的风险管控提供一定的建议。

In recent years, as global attention to climate change and sustainable development continues to grow, the advancement of a low-carbon economy and China’s “dual carbon” goals has garnered significant academic interest. The “dual carbon” strategy—achieved through both emission reduction and carbon sequestration—guides the economy toward a green and sustainable transformation. In this context, new energy vehicles (NEVs) play a pivotal role in both the energy revolution and carbon reduction. According to data, the market penetration rate of NEVs in China reached 31.6% in 2023 and continues to rise. However, intense industry competition has left many NEV enterprises facing financial difficulties, including fragile capital chains, ongoing operational challenges, and low profitability. Against this backdrop, accurately assessing the financial risks of NEV companies is not only essential for internal risk management but also a critical basis for investor decision-making. Therefore, it is imperative for NEV enterprises to establish sound financial risk prevention and control systems to enhance their resilience through early warning and precise management.

This study takes into account the current state of the NEV industry and the actual situation of Seres Group to identify the internal and external financial risks faced by the company. Based on the characteristics of NEV enterprises, the study selects indicators from four dimensions—profitability, operational efficiency, solvency, and growth potential—to construct a financial risk early warning model tailored to the sector. First, the paper outlines the research background and significance, briefly reviews the current literature on financial risk in the NEV industry and existing early warning models, and defines key concepts such as financial risk and financial risk early warning. Second, it analyzes the industry environment and the basic corporate profile of the case company, applying the Internal-External (IE) Matrix model to identify and assess its financial risks. The model finds that Seres faces internal risks primarily in the form of high R&D expenditures, a high debt-to-asset ratio, and excessive reliance on a single market. External risks include fluctuations in raw material prices, supply chain pressures, global trade barriers, and policy uncertainty. These risks are then categorized into profitability, operational, solvency, and growth-related risks. Next, the Analytic Hierarchy Process (AHP) is employed to select high-weight financial indicators. Significance testing, multicollinearity analysis, and Fisher discriminant analysis are used to construct a financial risk early warning model based on the F-score, which is further optimized to determine appropriate financial risk thresholds. This model is then used to conduct both horizontal and longitudinal risk analysis of Seres’ financial status. Finally, based on the findings, the study proposes internal and external risk mitigation strategies to offer practical recommendations for Seres’ financial risk management.

[1]Henri Fayol. Ceneraland Industrial Management[M]. Paris: S.R.L.Dunod,1916

[2]Mowbray, Blanchard Williams. lnsurance[M]. New York: Mc Graw Hill,1950

[3]James,Van H,Wachowicz J R.Fundamental of Financial Management[M].北京:清华大学出版社,2007.

[4]Whitake.Inancial markets,derivative securities,and systematic risks[J].Journal of Risk and Uncertainty,1996,32:271-286.

[5]Wachowicz J M.Fundamentals of Financial Management[J].New York:Prentice Hall.2014:334-335.

[6]Olson D L,W D.Enterprise risk management[J].Portable Mba Fifth Edition,2018,78(3):329.

[7]Computation Neural Computation; Findings from Taishan University Yields New Data onNeural Computation (Image Pattern Recognition In Identification of Financial Bills RiskManagement)[J].Journal of Robotics & Machine Learning,2020.

[8]Habib,A,Costa,M.D,Huang,H.J,etc.Determinants and consequences of financialdistress: review of the empirical literature[J].Accounting & Finance,2020,60(2):1023-1075.

[9]Sidorova Elena,Kostyukhin Yuri,Korshunova Lyudmila, Ulyanova Svetlana, ShinkevichAlexey, Ershova Irina, Dyrdonova Alena. Forming a Risk Management System Based on theProcess Approach in the Conditions of Economic Transformation[J].Risks,2022,10(5).

[10]Landi Giovanni Catello, Iandolo Francesca, Renzi Antonio, Rey Andrea. Embedding sustainability in risk management: The impact of environmental, social, and governance ratingson corporate financial risk[J]. Corporate Social Responsibility and Environmental Management,2022,29(4).

[11]Cherry Preston D, Asebedo Sarah. Personality traits and long-term care financial risksamong older Americans[J]. Personality and Individual Differences, 2022.

[12]Peculea A D,Sui Y. Financial risk identification and control of cross border merger and acquisitionenterprises[J]. Audit Financiar Journal,2018,14.

[13]Jeehan Almamy; John Aston; Leonard N. Ngwa. An evaluation of Altman's Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK[J].10.1016/j.jcorpfin.2015.12.009

[14]Michaela Kendall.Vehicles (NEVs) and clean Materials International, Fuel cell development forNew Energy air in China[J].Progress in Natural Science:2019:51002007118301679.

[15]Trussel. Assessing and ranking the financial risk of municipal governments[J].Journal of AppliedAccounting Research,risk of municipal 2020, 19 (1):78-80.

[16]Bu Q, Yang Z. Mode selection and risk estimation of financing in new energyautomobile enterprises[J]. Energy Reports, 2021, 7: 330-337

[17]Fernandes Simangunsong,Amandus Ivo Mario.Study on Possibility of Applying Risk Managementin Village Fund Management in Landak Regency Indonesia[J].Open. Journal of Social Sciences,2021(9):271-297.

[18]Fitzpatrick P.J. A Comparison of Ratios of Successful Industrials Enterpriseswith Those of Failed Firms[J].Certified Public Accountant, 1932,2:589-605

[19]William H.Beaver.Financial ratios as predictors of failure. Journal of Accounting Research,1966,4(3):71-111

[20]Edward Altman.Financial ratios,discriminate analysis and the Prediction of bankruptcy [J].Journal of Finance,1968(9):589-609.

[21]Edmister R.Financial rations as discriminant predictors of small businessfailure.Journal of Finance,1972(27):1277-1493

[22]James A Ohlson.Financial Ratios and the Probabilistic Prediction of Bankruptcy[J].Journal of Accounting Research,1980,19:109-131

[23]Odom M D,Sharda R.A neural network model for bankruptcy prediction. InternationalJoint Conference on Neural Networks,1990,6(02):163-168

[24]Altman E. Corporate distress diagnosis: Comparisons neural networks[J].Journal ofBanking and Finance, 1994(3):505-529.

[25]Hill N T,Perry S E,Andes S. Evaluating firms in financial distress: An event history analysis[J]. Journal ofApplied Business Research (JABR), 1996, 12(3): 60-71.

[26]Spathis,Doumpos.Detecting falsifed financial statement:a comparative study usingmulticriteria analysis and multivariates statistical techniques.The EuropeanAccounting Review,2002,(11)：509-535

[27]Julie Fitzpatrick,Joseph P.Ogden.The Detection and Dynamics of Financial Distress[J].International Review of Finance,2011,11(1):87–121.

[28]Georgios Marinakos,Sophia Daskalaki, Theodoros Ntrinias. Defensive financial decisions support for retailersin Greek pharmaceutical industry[J]. Central European Journal of operations research.2013,(08):123-132.

[29]Mangla S K, Kumar P, Barua M K. Risk analysis in green supply chain using fuzzyAHP approach: A case study[J].Resources Conservation&Recycling, 2015, 104:375-390.

[30]Alnoor Bhimani, Mtholi Neube. Investment decision-making of enterprisement[J].Journal of Accounting and Public Policy, 2017(08):390-408.

[31]Sona Jiraskova.Financial risk management[J].Land Forces Academy Review,2017,(10):276-280.

[32]Kurum E ,Weber G W ,Iyigun C.Early warning on stock market bubbles via methods of optimization, clusteringand inverse problems[J]. Annals of Operations Research, 2018, 260(1-2):293-320.

[33]John M. Trussel, Patricia A. Patrick. Assessing and ranking the financial risk ofmunicipal[J].Journal of Applied Accounting Research, 2018,19(1):78-80.

[34]王进朝,张永仙.企业生命周期、内部控制质量与财务风险[J].会计之友,2020(19):

[35]黄祉飞.企业财务风险的识别与内部控制探讨[J].财经界,2021(19):107-108.

[36]王守垠.企业财务风险识别与管控探讨[J].财会学习,2022(23):35-37.

[37]薛茜文.关于企业财务风险识别和防范的探讨[J].中国总会计师,2022,(12):101-103.

[38]彭奇瑞.企业财务风险识别及应对措施分析[J].投资与创业,2023(07):96-98.

[39]黄丹萍.企业财务风险识别与防范研究[J].纳税,2023(15):65-66.

[40]章激扬,段继红,李珊珊.新能源补贴、研发支出与企业资产收益率——基于新能源车企的实证分析[J].科技管理研究,2022,42(13):80-88.

[41]熊勇清,熊裕康,张志剑.媒体关注对新能源车企声誉投资影响的实证研究[J].中南大学学报(社会科学版),2022,28(03):97-109.

[42]郭晓丹,王帆.“双碳”目标下政府补贴、需求替代与减排效应——来自中国乘用车市场的证据[J].数量经济技术经济研究,2024,41(02):131-150.20231214.005.

[43]张智全,付甜甜,万力,等.财政补贴对我国新能源汽车产业发展的影响[J].电源技术,2024,48(04):591-594.

[44]王超,张鑫语.补贴退坡会对中国新能源汽车需求产生影响吗?——基于西安的实证[J].生态经济,2024,40(08):89-96.

[45]陈晓红,杨柠屹,周艳菊.后疫情时代新能源车产业的供应链共建策略研究[J/OL].中国管理科学,1-12[2024-09-27].

[46]赵春萍,林春山,李冀中.疫情下的汽车行业发展[J].中国金融,2021(07):95-96.

[47]王晓玉,苏普远.基于SCP视角的动力电池行业分析[J].物流工程与管理,2020,42(07):123-126+192.

[48]况新亮,刘垂祥,熊朋.锂离子电池产业分析及市场展望[J].无机盐工业,2022,54(08):12-19+32.DOI:10.19964/j.issn.1006-4990.2022-0002.

[49]周首华,杨济华,王平.论财务危机的预警分析—F分数模式[J].会计研究,1996(08):8-11.

[50]陈静.上市公司财务恶化预测的实证分析[J].会计研究,1999,(04):32-39.

[51]杨淑娥,黄礼.基于BP神经网络的上市公司财务预警模型[J].系统工程理论与实践,2005,(01):12-18+26.

[52]周兴荣.企业集团财务风险预警研究[J].财会月刊,2007,(32):47-50.DOI:10.19641/j.cnki.42-1290/f.2007.32.018.

[53]刘磊,郭岩.基于RBF神经网络的物流企业财务风险预警评价[J].财会月刊,2012,(09):59-61.DOI:10.19641/j.cnki.42-1290/f.2012.09.018.

[54]陈超,李君.化工类上市公司财务危机预警研究——基于BP人工神经网络模型[J].会计之友,2012,(20):83-86.

[55]许祎航.基于熵值法与功效系数法的太龙药业财务风险预警研究[D].湖南大学,2014.

[56]宫兴国,张博,吴琪.基于GM(2,1)灰色复合模型的财务风险预测[J].统计与决策,2015(17):179-182.DOI:10.13546/j.cnki.tjyjc.2015.17.051.

[57]黄电.PPP项目财务风险度量与控制研究[J].财会月刊,2018(21):85-90.DOI:10.19641/j.cnki.42-1290/f.2018.21.012.

[58]杨贵军,杜飞,贾晓磊.基于首末位质量因子的BP神经网络财务风险预警模型[J].统计与决策,2022,38(03):166-171.DOI:10.13546/j.cnki.tjyjc.2022.03.031.

[59]严莉红.基于LSTM神经网络的饲料企业财务风险预警模型构建[J].饲料研究,2023,46(03):130-134.DOI:10.13557/j.cnki.issn1002-2813.2023.03.026.

[60]杨海志.会计信息可比性、营运资本管理与企业现金流风险[J].财会通讯,2024(05):43-47.DOI:10.16144/j.cnki.issn1002-8072.2024.05.009.