CN113763184A - 一种碳资产评估方法 - Google Patents
一种碳资产评估方法 Download PDFInfo
- Publication number
- CN113763184A CN113763184A CN202110988384.2A CN202110988384A CN113763184A CN 113763184 A CN113763184 A CN 113763184A CN 202110988384 A CN202110988384 A CN 202110988384A CN 113763184 A CN113763184 A CN 113763184A
- Authority
- CN
- China
- Prior art keywords
- carbon
- emission reduction
- evaluating
- asset
- carbon asset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- Technology Law (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Educational Administration (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明公开了一种碳资产的评估方法,涉及碳资产评估领域。该方法包括:(1)构建碳资产价值影响因素样本数据矩阵;(2)将样本数据矩阵进行去均值归一化处理;(3)构建lasso回归模型,并求解;(4)长短期记忆神经网络模型训练,直到得到最优的输出结果,训练结束;(5)利用长短期记忆神经网络对碳资产进行评估。本发明的方法服务于碳金融市场减排作用的发挥和市场交易机制的完善,有助于提高减排资金的配置效率,促进减排主体以较低的成本促进自身减排目标的实现和减排责任的落实,更好地服务于实体企业的碳减排行动,推动二氧化碳减排实践的落实,从而切实减少污染气体的排放。
Description
技术领域
本发明涉及碳资产评估领域,具体涉及一种碳资产的评估方法。
背景技术
全国碳排放权交易市场于2017年7月16日正式鸣锣开市,占全国碳排放40%以上的超2000家发电企业作为首批交易主体走进该市场,首日成交均价51.23元/吨,成交量410.40万吨,成交额逾2.1亿元。纳入首批碳市场覆盖的企业碳排放量超过40亿吨二氧化碳,这意味着中国的碳排放权交易市场一经启动,就成为全球覆盖温室气体排放量规模最大的碳市场。在此背景下,碳金融将取得快速发展,有关碳资产的配置、管理、投资等涉及到估值层面的需求将迅速上涨,碳资产价值的估值不仅成为碳交易市场发展的重要因素,更是对生态文明建设产生了重要的影响。碳资产是低碳经济下的一种新型重要资产,需要对它进行合理评估以发现其内在价值,为企业等组织进行经营决策提供专业的价值标准和参考依据,以推动碳资产交易市场的发展,促进我国节能减排、环境治理政策的实施,助力我国碳达峰、碳中和目标的实现。
现有碳排放权交易过程中存在的不足有:(1)碳金融资产定价研究主要基于收益率低阶矩属性视角,采用影响因素分析法,研究“均值-方差”二维框架下碳价及其定价因子市场间的收益传导和波动溢出关系,然而这种关系的刻画仅限于解释碳价低阶矩属性的溢价机制,并没有从高阶矩属性解释碳价的影响机制,难以真实反映碳金融资产价格运行规律,相关定价结论的准确性存在质疑;(2)忽略了对碳金融资产与其定价因子间风险传染关系的研究,难以对碳金融资产溢价波动提供更有力的证据解释;(3)掩盖了金融市场收益序列本身所具有的波动趋势异质性特征,且无法全面地识别碳金融资产风险传染的诱发因素,隐含了一种不合理的假设,未立足于碳金融市场特殊的波动趋势异质性特征,难以满足对碳金融市场波动趋势异质性特征的刻画。
发明内容
本发明的目的在于针对上述现有技术存在的不足,提供一种碳资产的评估方法,该方法为发现碳资产的内在价值,优化资源配置,促进碳资产的有序流转,保障流转方的合法权益提供了一种更为准确、可靠的技术方案。
为了实现上述目的,本发明采用的技术方案如下:
一种碳资产的评估方法,包括:
(1)构建碳资产价值影响因素样本数据矩阵;
(2)将样本数据矩阵进行去均值归一化处理;
(3)构建lasso回归模型,并求解;
(4)长短期记忆神经网络模型训练,直到得到最优的输出结果,训练结束;
(5)利用训练好的长短期记忆神经网络模型对碳资产进行评估。
进一步的,步骤(1)中,所述碳资产价值影响因素样本数据矩阵为X={x1,x2,...,xn},标签向量Y={y1,y2,...,yn},其中,矩阵X中元素xi为d维向量,Y向量中每个元素为实数值。
进一步的,步骤(1)中,所述影响因素包括但不限于历史碳权价格、法兰克福DAX指数、核证减排量期货合约价格、欧盟碳排放量期货合约价格、碳减排成本。
进一步的,步骤(3)中,所述求解采用LASSO方法求解,并利用贝叶斯信息准则选取最优解。
进一步的,步骤(4)中,训练时将步骤(3)得到的碳资产价值影响因素的变量最优结果输入到长短期记忆神经网络模型,输出为企业碳资产的实际成交价。
进一步的,步骤(5)中,评估时将采集的实时碳资产价值影响因素数据输入到训练好的长短期记忆神经网络模型,经数据处理,输出企业碳资产评估结果,完成评估。
综上所述,由于采用了上述技术方案,本发明的有益技术效果是:本发明的碳资产评估方法服务于碳金融市场减排作用的发挥和市场交易机制的完善,有助于提高减排资金的配置效率,促进减排主体以较低的成本促进自身减排目标的实现和减排责任的落实,更好地服务于实体企业的碳减排行动,推动二氧化碳减排实践的落实,从而切实减少污染气体的排放;此外,还可为碳金融市场投资决策提供依据,从而促进碳交易市场效率的提升以及碳金融产品不断丰富和创新,吸引更多市场参与者,提高碳交易市场的活跃度,为碳资产供求企业实现经济效益和生态效益最优化。
附图说明
图1为本发明一实施例的碳资产的评估方法的流程图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅用以解释本发明,并不用于限定本发明。
实施例
请参阅附图1所示,本实施例提供了一种碳资产的评估方法,包括:
(1)构建碳资产价值影响因素样本数据矩阵;
碳资产价值影响因素样本数据矩阵为X={x1,x2,...,xn},标签向量Y={y1,y2,...,yn},其中,矩阵X中元素xi为d维向量,Y向量中每个元素为实数值;
所述影响因素包括但不限于历史碳权价格、法兰克福DAX指数、核证减排量期货合约价格、欧盟碳排放量期货合约价格、碳减排成本;
(2)将样本数据矩阵进行去均值归一化处理;
(3)构建lasso回归模型,采用LASSO方法求解,并利用贝叶斯信息准则选取最优解;
(4)长短期记忆神经网络模型训练,直到得到最优的输出结果,训练结束;
训练时将步骤(3)得到的碳资产价值影响因素的变量最优结果输入到长短期记忆神经网络模型,输出为企业碳资产的实际成交价;
(5)利用训练好的长短期记忆神经网络模型对碳资产进行评估。
评估时将采集的实时碳资产价值影响因素数据输入到训练好的长短期记忆神经网络模型,经数据处理,输出企业碳资产评估结果,完成评估。
以上所述仅为本发明的较佳实施例,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。
Claims (6)
1.一种碳资产的评估方法,其特征在于,包括:
(1)构建碳资产价值影响因素样本数据矩阵;
(2)将样本数据矩阵进行去均值归一化处理;
(3)构建lasso回归模型,并求解;
(4)长短期记忆神经网络模型训练,直到得到最优的输出结果,训练结束;
(5)利用训练好的长短期记忆神经网络模型对碳资产进行评估。
2.根据权利要求1所述碳资产的评估方法,其特征在于:步骤(1)中,所述碳资产价值影响因素样本数据矩阵为X={x1,x2,...,xn},标签向量Y={y1,y2,...,yn},其中,矩阵X中元素xi为d维向量,Y向量中每个元素为实数值。
3.根据权利要求2所述碳资产的评估方法,其特征在于:步骤(1)中,所述影响因素包括但不限于历史碳权价格、法兰克福DAX指数、核证减排量期货合约价格、欧盟碳排放量期货合约价格、碳减排成本。
4.根据权利要求3所述碳资产的评估方法,其特征在于:步骤(3)中,所述求解采用LASSO方法求解,并利用贝叶斯信息准则选取最优解。
5.根据权利要求1所述碳资产的评估方法,其特征在于:步骤(4)中,训练时将步骤(3)得到的碳资产价值影响因素的变量最优结果输入到长短期记忆神经网络模型,输出为企业碳资产的实际成交价。
6.根据权利要求1所述碳资产的评估方法,其特征在于:步骤(5)中,评估时将采集的实时碳资产价值影响因素数据输入到训练好的长短期记忆神经网络模型,经数据处理,输出企业碳资产评估结果,完成评估。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110988384.2A CN113763184A (zh) | 2021-08-26 | 2021-08-26 | 一种碳资产评估方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110988384.2A CN113763184A (zh) | 2021-08-26 | 2021-08-26 | 一种碳资产评估方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113763184A true CN113763184A (zh) | 2021-12-07 |
Family
ID=78791308
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110988384.2A Pending CN113763184A (zh) | 2021-08-26 | 2021-08-26 | 一种碳资产评估方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113763184A (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117709945A (zh) * | 2023-12-19 | 2024-03-15 | 广州番禺职业技术学院 | 一种基于数字货币技术的碳金融定向支付系统 |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105975799A (zh) * | 2016-06-01 | 2016-09-28 | 广东电网有限责任公司电力科学研究院 | 一种碳排放量计算方法及系统 |
CN107239855A (zh) * | 2017-05-23 | 2017-10-10 | 华中科技大学 | 一种基于lstm模型的股票预测方法和系统 |
CN109902874A (zh) * | 2019-02-28 | 2019-06-18 | 武汉大学 | 一种基于深度学习的微电网光伏发电短期预测方法 |
US20190325524A1 (en) * | 2018-04-23 | 2019-10-24 | State Street Corporation | Techniques for accurate evaluation of a financial portfolio |
CN111222992A (zh) * | 2020-01-17 | 2020-06-02 | 大连大学 | 一种基于注意力机制的长短期记忆神经网络的股票价格预测方法 |
CN111832814A (zh) * | 2020-07-01 | 2020-10-27 | 北京工商大学 | 一种基于图注意力机制的空气污染物浓度预测方法 |
US20200372588A1 (en) * | 2019-05-20 | 2020-11-26 | Singularity Energy, Inc. | Methods and systems for machine-learning for prediction of grid carbon emissions |
CN112906974A (zh) * | 2021-03-11 | 2021-06-04 | 东南大学 | 一种负荷电量及其碳排放预测与校验方法 |
CN112990569A (zh) * | 2021-03-11 | 2021-06-18 | 浪潮云信息技术股份公司 | 一种水果价格预测方法 |
CN113159835A (zh) * | 2021-04-07 | 2021-07-23 | 远光软件股份有限公司 | 基于人工智能的发电侧电价报价方法、装置、存储介质及电子设备 |
-
2021
- 2021-08-26 CN CN202110988384.2A patent/CN113763184A/zh active Pending
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105975799A (zh) * | 2016-06-01 | 2016-09-28 | 广东电网有限责任公司电力科学研究院 | 一种碳排放量计算方法及系统 |
CN107239855A (zh) * | 2017-05-23 | 2017-10-10 | 华中科技大学 | 一种基于lstm模型的股票预测方法和系统 |
US20190325524A1 (en) * | 2018-04-23 | 2019-10-24 | State Street Corporation | Techniques for accurate evaluation of a financial portfolio |
CN109902874A (zh) * | 2019-02-28 | 2019-06-18 | 武汉大学 | 一种基于深度学习的微电网光伏发电短期预测方法 |
US20200372588A1 (en) * | 2019-05-20 | 2020-11-26 | Singularity Energy, Inc. | Methods and systems for machine-learning for prediction of grid carbon emissions |
CN111222992A (zh) * | 2020-01-17 | 2020-06-02 | 大连大学 | 一种基于注意力机制的长短期记忆神经网络的股票价格预测方法 |
CN111832814A (zh) * | 2020-07-01 | 2020-10-27 | 北京工商大学 | 一种基于图注意力机制的空气污染物浓度预测方法 |
CN112906974A (zh) * | 2021-03-11 | 2021-06-04 | 东南大学 | 一种负荷电量及其碳排放预测与校验方法 |
CN112990569A (zh) * | 2021-03-11 | 2021-06-18 | 浪潮云信息技术股份公司 | 一种水果价格预测方法 |
CN113159835A (zh) * | 2021-04-07 | 2021-07-23 | 远光软件股份有限公司 | 基于人工智能的发电侧电价报价方法、装置、存储介质及电子设备 |
Non-Patent Citations (2)
Title |
---|
喻胜华等: ""基于Lasso和BP神经网络的组合预测及其应用——以居民消费支出预测为例"", 《财经理论与实践》, vol. 37, no. 199, pages 123 - 128 * |
胡锐: ""机器学习模型在中国A股市场的应用"", 《中国优秀硕士学位论文全文数据库基础科学辑》, pages 002 - 587 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117709945A (zh) * | 2023-12-19 | 2024-03-15 | 广州番禺职业技术学院 | 一种基于数字货币技术的碳金融定向支付系统 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cull et al. | Who gets credit? The behavior of bureaucrats and state banks in allocating credit to Chinese state-owned enterprises | |
Zhai et al. | Whale Optimization Algorithm for Multiconstraint Second‐Order Stochastic Dominance Portfolio Optimization | |
Vuković et al. | The effect of working capital management on profitability: evidence from southeast europe | |
Hong et al. | Does digital transformation of enterprises help reduce the cost of equity capital | |
Obadeyi | Microfinance banking and development of small business in emerging economy: Nigerian Approach | |
CN113763184A (zh) | 一种碳资产评估方法 | |
Hsieh et al. | Working capital management and profitability of publicly traded Chinese companies | |
Cherep et al. | Relationship of investment development and innovative activity of industrial enterprises | |
Ma et al. | Empirical analysis and optimization of capital structure adjustment. | |
Stewart | MULTINATIONAL COMPANIES AND TRANSFER PRICING. | |
Zhang | [Retracted] Dynamic Index Optimal Investment Strategy Based on Stochastic Differential Equations in Financial Market Options | |
Nagy et al. | An Analysis of the Financial Health of Companies Concerning the Business Environment of the V4 Countries | |
Ali et al. | Influence of Profitability on Dividend Payout in Deposit-Taking Savings and Credit Co-Operatives (SACCOs) in Kenya | |
Verbivska et al. | Taxation strategy for small businesses in the context of digitalization | |
Adeyinka et al. | Implications of Development Bank Finance on the Growth and Development of Msmes in Nigeria (2010-2017) | |
Alsaedi | Examining the Role of Green Investments in Promoting Sustainable Business Practices and Development | |
PUROHIT | Debt or Retained Earnings: Whichever is Optimum for the Growth of the Firm | |
Artikov et al. | Analysis And Optimization Of Product Costs And Expenses In Enterprises | |
Zeng | Influence of internet finance on the financing of small and medium-sized enterprises in China | |
Li | Research on Asset Allocation of Insurance Companies Based on Mean-Variance Model | |
Shuaib et al. | Financial openness and economic growth in Nigeria (1960-2014) | |
Huang et al. | Dose Digital Innovation Produce Better ESG Performance? An Empirical Test of Shenzhen A-Listed Firms in China | |
Zhang et al. | Human-Computer Interaction Perspectives on Small and Medium-Sized Enterprises Financing Mode and Structure Analysis Using Artificial Intelligence | |
Zhang et al. | The Impact of Accounting Information Quality on Cost of Debt Capital | |
Ma et al. | Fundamental Signals, Machine Learning and Stock Volatility Forecasting |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |