Pith. sign in

REVIEW 5 major objections 7 minor 119 references

Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future

T0 review · 5 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that carbon credits become practically manageable only when you pair carbon price forecasting with corporate emission forecasting, and that this pairing is what a responsible fintech approach to carbon management needs.

desk verdict A useful synthesis of carbon credit computing research, but its 'first systematic review' claim currently rests on a methodology section that doesn't survive close reading. read the letter →

arxiv 2501.14750 v3 pith:PUQB27M4 submitted 2024-12-22 cs.CY cs.LG

Qingwen Zeng , Hanlin Xu , Nanjun Xu , Zhenghao Zhao , Joakim Westerholm , Flora Salim , Junbin Gao , Huaming Chen This is my paper · ORCID
classification cs.CYcs.LG
keywords carboncreditspricepredictioncorporateemissiondisclosurefintechsystematicliteraturereviewmanagementcostESGandmarketvalue
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that carbon credits become a practical management tool only when computing is brought in: organizations need to know both what their emissions will be and what credits will cost. Reviewing evidence from several countries, it argues that non-disclosure does not protect firms—it is linked to lower market value, worse financial performance, and higher loan spreads—while transparent disclosure plus predictive algorithms creates a virtuous cycle. It surveys the factors that move carbon credit prices and the current crop of price-prediction hybrids, then the newest corporate emission prediction models. Its concluding proposal is that combining the two predictions enables corporate carbon management cost forecasting, which in turn supports purchasing strategy, valuation, and risk assessment. The paper claims to be the first systematic review of carbon credits from a computing-and-engineering perspective.

What carries the argument

The argument runs on a two-prediction engine: carbon credit price prediction and corporate carbon emission prediction. Price prediction typically decomposes the price series with signal-processing techniques such as Empirical Mode Decomposition, Complete Ensemble Empirical Mode Decomposition, or wavelet transforms, selects external drivers such as energy prices, macroeconomic indicators, policy events, and air quality, then trains hybrid machine-learning models such as LSTM, GRU, SVR, LightGBM, or transformers with metaheuristic optimization. Emission prediction learns firm-level footprints from external financial and operational data using regression, support vector machines, XGBoost, and deep networks. The forward-looking mechanism is their integration: forecast emissions times forecast credit price equals carbon management cost, which can feed discounted-cash-flow valuation and carbon risk ratings.

What would settle it

A later literature search with the paper's own inclusion criteria that surfaces peer-reviewed fintech-focused carbon-credit reviews or computing-solution papers published before December 2024 and absent here would falsify the first-review claim; on the prediction side, a common benchmark evaluation of the surveyed emission models on a single firm-level dataset would show whether the reported accuracy rankings hold.

Watch

Extended reading notes

Core claim

The central claim is that a computing-and-engineering view of carbon credits is both missing and needed. The paper reviews three bodies of evidence—disclosure effects, carbon price prediction, and corporate emission prediction—and asserts this is the first systematic review to cover all three with a fintech focus. Its finding is that carbon transparency is not a cost but a risk-reduction strategy: price prediction lets firms buy credits when they are cheap, emission prediction lets investors and governments see through non-disclosure, and joining the two produces corporate carbon management cost forecasts. That integration, the paper argues, lays the foundation for quantitative research linking carbon management practices to corporate market value and financial performance.

Load-bearing premise

The load-bearing premise is that the surveyed corpus is complete and representative; if the August 2023 search date coexisting with 2024 citations and the inclusion of non-peer-reviewed items make the corpus unreproducible, the review's map of methods and its first-review claim lose force.

Editorial extensions

If this is right

  • Organizations can time carbon credit purchases to predicted price lows, lowering compliance costs and making voluntary disclosure more attractive.
  • Investors and regulators can cross-check disclosed emission figures against model predictions, reducing information asymmetry in ESG assessment.
  • Combining price and emission forecasts yields corporate carbon management cost projections that support budget planning rather than after-the-fact accounting.
  • Financial institutions could fold predicted carbon costs into credit and insurance pricing, consistent with the loan-spread evidence the review reports.
  • Future quantitative work can estimate how carbon management practices feed through to market value, financing costs, and long-term financial performance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The unstated but natural next step is to treat the predicted carbon management cost as a priced risk factor in credit ratings, since the reviewed loan-spread evidence already implies banks charge for carbon risk.
  • A testable extension is a standardized benchmark suite for corporate emission predictors, so reported gains such as the accuracy improvement over existing methods can be compared across markets and industries.
  • The same two-prediction machinery could be reversed: regulators could use emission predictions to target audits at firms whose disclosed figures deviate most from model estimates.
  • Future work could stress-test carbon cost forecasts under alternative price scenarios, effectively building scenario-based carbon risk ratings for individual firms.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 7 minor

Summary. The paper presents itself as the first systematic review of carbon credits with a focus on computing solutions and engineering practices for fintech applications. It has three main parts: (i) a review of the financial and market consequences of corporate carbon emission disclosure and non-disclosure, drawing on studies from the US, UK, Australia, Japan, Indonesia, Malaysia, and South Korea; (ii) a review of factors influencing carbon credit prices, summarized in two large tables, and a survey of state-of-the-art carbon price prediction algorithms; and (iii) a review of corporate carbon emission prediction methods, both empirical and machine-learning based. The paper concludes by proposing future directions, notably the integration of carbon price and emission predictions to forecast corporate carbon management costs. The stated central claim is that this is the first systematic review covering the various aspects of carbon credits, with a particular focus on computing solutions and engineering practices.

Significance. If the central claim is accepted, the paper would provide a useful interdisciplinary map for researchers and practitioners working at the intersection of carbon markets, corporate disclosure, and fintech. Its strengths include a clear statement of research questions, structured tabular syntheses of price drivers and prediction methods, and a concrete proposal for future quantitative research on corporate carbon management costs. The paper also explicitly acknowledges cross-country heterogeneity in the disclosure literature, which tempers some of its generalizations. However, the review's value and its 'first systematic review' claim rest on the reproducibility and completeness of the literature corpus, and the manuscript currently has unresolved inconsistencies in the reported search methodology and inclusion criteria. These issues must be addressed before the review can be considered a reliable systematic map of the field.

major comments (5)
  1. [§2.1] The methodology states that 'The results are collected in August 2023,' yet the reference list includes numerous publications dated 2024, including refs. [35], [42], [86], [90], [93], [112], [113], [114], [115], [117], and [118], with no online-first or in-press qualification. This internal inconsistency makes the corpus non-reproducible: a reader cannot tell whether the search date is inaccurate or whether the corpus was supplemented after the stated search without documentation. This is load-bearing because the 'systematic review' and 'first' claims require a well-defined, reproducible corpus.
  2. [§2.2] Exclusion Criterion 1 states that non-peer-reviewed literature is excluded, including 'news articles, blog posts, and informal white papers,' yet the review cites SSRN preprints [2], [9], [14], [19], [23] and an MSCI ESG Research issue brief [104]. The MSCI brief is used in §6.1 as substantive evidence for the comparative claim that the MSCI ESG Research model outperforms the EIO-LCA model. If the stated exclusion criterion were applied, parts of the evidence base for §3 and §6.1 would change. The authors should either justify the inclusion of these sources as exceptions or revise the criteria to match the actual corpus.
  3. [§2.3] The selection process is not described at a level that permits replication. The search strings are only described as 'constructed using key phrases from our research questions,' with no database-specific search strings, no record counts, no screening or exclusion decisions, and no PRISMA-style flow diagram. The Quality Assessment criteria (QA1–QA5) are listed, but no results of applying these criteria are reported, so it is unclear how many papers were included or excluded at each stage. This is not a minor omission: the 'systematic' claim is central, and the current description does not allow the corpus to be audited.
  4. [§4, Table 1] Table 1 contains two directly contradictory rows under 'Coal': one states that rising coal prices lead industries to seek lower-carbon alternatives, reducing demand for carbon credits and thus lowering carbon prices; the other states that rising coal prices are associated with increased demand for traditional energy, higher emissions, and higher carbon prices. The narrative text in §4 acknowledges both mechanisms but does not provide a synthesis, a dominance condition, or a market-specific qualification. Since Contribution 2 claims these factors allow organisations to 'qualitatively predict price trends,' the unresolved contradiction leaves the practical guidance ambiguous and should be addressed explicitly.
  5. [Abstract and §9] The claim that this is 'the first systematic review covering the various aspects of carbon credits, with a particular focus on computing solutions and engineering practices' is a completeness claim. It is not supported by the reported search, which is non-reproducible per the comments above, and the comparison in §8 examines only five prior reviews, with no demonstration that a broader search for prior comprehensive reviews was conducted. At minimum, the claim should be qualified (e.g., 'to our knowledge') and the authors should provide stronger evidence of search coverage, or the claim should be softened to avoid asserting priority.
minor comments (7)
  1. [Abstract and §3] The paper repeatedly presents the conclusions of cited studies as 'our findings' or 'this study finds,' e.g., in the Abstract and in the bullet points at the end of §3. Since the paper is a review, these statements should be attributed to the reviewed literature (e.g., 'the reviewed studies indicate that...').
  2. [§1] There is a typo in the Introduction: 'it role in mitigating global temperature rise' should read 'its role in mitigating global temperature rise.'
  3. [§4] In the paragraph following Table 1, 'Economic growth often acorganisations increased industrial production' appears to be a typographical error; the intended text is likely 'Economic growth often accompanies increased industrial production.'
  4. [Table 2] In Table 2, the row for 'Market Stability Reserve (MSR)' shows 'Market Stability Reserve (MSR) N-A MEMD, V AR, VEC Models [39],' where 'N-A' appears to be a misaligned table entry. This should be corrected for clarity.
  5. [Figures 1–3] The text references Figures 1, 2, and 3, but the manuscript text provided does not include the actual figure images; the captions are present but the figures are not embedded. The figures should be properly included and explained in the text.
  6. [References] Reference [5] is a non-academic web source ('Climatepolicyinfohub.eu'), which is inconsistent with the paper's stated exclusion of informal white papers; the authors should either replace it with an academic source or, if the source is essential, explicitly justify its inclusion.
  7. [Title page] The title page contains odd letter-spacing in 'T HE P RACTICES , I MPLICATIONS , AND FUTURE'; this is likely a formatting artifact and should be cleaned up in the final version.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a literature synthesis with no self-citations and no derivation or prediction that reduces to its own inputs.

full rationale

This manuscript is a systematic literature review, not an empirical derivation. Its sections summarize external studies: Section 3 reports findings from cited country-level studies on disclosure and firm value, Section 4 tabulates factors influencing carbon prices with references to econometric and machine-learning papers, Section 5 reviews price-prediction algorithms from the literature, and Section 6 reviews corporate emission prediction models. No equation in the paper fits a parameter and then reports that fit as a prediction, and no reviewed algorithm is derived from the paper's own assumptions. The reference list contains no self-citations by the authors of this arXiv paper, so there is no self-citation chain carrying the argument. The central claim of being 'the first systematic review covering the various aspects of carbon credits, with a particular focus on computing solutions and engineering practices' is a novelty and completeness claim supported by the comparison in Section 8 against other reviews; whether the corpus is complete or reproducible is a validity concern, not circularity. The stated August 2023 search date with 2024 references, and the exclusion of non-peer-reviewed literature alongside SSRN and MSCI citations, are internal methodological inconsistencies, but they do not make any conclusion equivalent to its input by construction. Therefore, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities; it is a literature review. The central claims rest on the completeness and validity of the cited literature and on the search methodology described in Section 2.

assumptions (3)
  • domain assumption The selected databases (Google Scholar, Scopus, IEEE Xplore, Science Direct) and search terms provide comprehensive coverage of relevant literature.
    Section 2.1 lists data sources and timeframe but provides no validation of coverage completeness or exact search strings.
  • domain assumption The findings of the cited empirical studies are valid and generalizable across markets and periods.
    Sections 3 and 4 synthesize conclusions from cited papers without independent verification or meta-analysis.
  • domain assumption The claim of being the first systematic review on computing solutions for carbon credits is accurate.
    Section 8 compares with several recent reviews but does not exhaustively demonstrate the absence of prior work.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future." pith.science (2026). https://pith.science/paper/PUQB27M4

@misc{pith2026250114750,
  author       = {Pith},
  title        = {Pith review of: Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PUQB27M4}},
  note         = {Machine review of arXiv:2501.14750}
}
read the original abstract

Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage their carbon footprint. Despite their growing importance across sectors, fully leveraging carbon credits remains challenging. This study explores engineering practices and fintech solutions to enhance carbon emission management. We first review the negative impacts of carbon emission non-disclosure, revealing its adverse effects on financial stability and market value. Organizations are encouraged to actively manage emissions and disclose relevant data to mitigate risks. Next, we analyze factors influencing carbon prices and review advanced prediction algorithms that optimize carbon credit purchasing strategies, reducing costs and improving efficiency. Additionally, we examine corporate carbon emission prediction models, which offer accurate performance assessments and aid in planning future carbon credit needs. By integrating carbon price and emission predictions, we propose research directions, including corporate carbon management cost forecasting. This study provides a foundation for future quantitative research on the financial and market impacts of carbon management practices and is the first systematic review focusing on computing solutions and engineering practices for carbon credits.

Figures

Figures reproduced from arXiv: 2501.14750 by the authors.

Figure 1
Figure 1. Factors Influencing Carbon Emission Disclosure [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Impact of carbon emissions disclosure 4 For Engineering: Factors Influencing Carbon Credit Prices Building on the previous conclusion, organisations that engage in transparent carbon emission disclosure and proactive carbon management not only maintain a competitive edge in the market and earn the trust of capital markets and financial institutions, but also improve financial performance, comply with government regu… view at source ↗
Figure 3
Figure 3. Evolutionary Trends in Corporate Carbon Emission Prediction Algorithms [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

119 extracted references · 73 canonical work pages

  1. [35]

    Time varying characteristics of factors affecting carbon price in emission trading scheme in china: Evidence from sv-tvp-var approach

    Fang Hu, Yubo Li, Xianmei Wang, and David Lee Cook. Time varying characteristics of factors affecting carbon price in emission trading scheme in china: Evidence from sv-tvp-var approach. Journal of Infrastructure, Policy and Development, 8(7):3793, 2024

    2024

  2. [42]

    Analysis of china’s carbon market price fluctuation and international carbon credit financing mechanism using random forest model

    Cuiling Song. Analysis of china’s carbon market price fluctuation and international carbon credit financing mechanism using random forest model. PLOS ONE, 19(3):e0294269, 2024

    work page 2024

  3. [86]

    Causal carbon price interval prediction using lower upper bound estimation combined with asymmetric multi-objective evolutionary algorithm and long short-term memory

    Jujie Wang, Maolin He, and Weiyi Jiang. Causal carbon price interval prediction using lower upper bound estimation combined with asymmetric multi-objective evolutionary algorithm and long short-term memory. Expert Systems with Applications, 236:121286, 2024

    work page 2024

  4. [90]

    Forecasting carbon price trends based on an interpretable light gradient boosting machine and bayesian optimization

    Shangkun Deng, Jiankang Su, Yingke Zhu, Yiting Yu, and Chongyi Xiao. Forecasting carbon price trends based on an interpretable light gradient boosting machine and bayesian optimization. Expert Systems with Applications, 242:122502, 2024

    work page 2024

  5. [93]

    A cnn-lstm based deep learning model with high accuracy and robustness for carbon price forecasting: A case of shenzhen’s carbon market in china

    Hanxiao Shi, Anlei Wei, Xiaozhen Xu, Yaqi Zhu, Hao Hu, and Songjun Tang. A cnn-lstm based deep learning model with high accuracy and robustness for carbon price forecasting: A case of shenzhen’s carbon market in china. Journal of Environmental Management, 352:120131, 2024

    work page 2024

  6. [112]

    Carbonnet: Enterprise-level carbon emission prediction with large-scale datasets

    Jinghua Tang, Nan Fang, Lanqing Yang, Yuqiao Pei, Ran Wang, Dian Ding, Yu Lu, and Guangtao Xue. Carbonnet: Enterprise-level carbon emission prediction with large-scale datasets. In International Conference on Intelligent Computing, pages 411–422. Springer, 2024

    work page 2024

  7. [113]

    A framework for enterprise assessment of carbon performance using support vector machines

    Yijun Shou. A framework for enterprise assessment of carbon performance using support vector machines. Soft Computing, 28(1):641–660, 2024

    work page 2024

  8. [114]

    Research on carbon emission prediction of coal combustion in power generation enterprises based on rough set and grey svm

    Yong Zhao, Yong Zhou, Fanliang Meng, and Huahai Qiu. Research on carbon emission prediction of coal combustion in power generation enterprises based on rough set and grey svm. In Intelligent Computing Technology and Automation, pages 174–181. IOS Press, 2024

    work page 2024

  9. [115]

    Co2 emission prediction based on carbon verification data of 17 thermal power enterprises in gansu province

    Wei Shi, Jiapeng Yang, Fuwei Qiao, Chengyuan Wang, Bowen Dong, Xiaolong Zhang, Sixue Zhao, and Weijuan Wang. Co2 emission prediction based on carbon verification data of 17 thermal power enterprises in gansu province. Environmental Science and Pollution Research, 31(2):2944–2959, 2024

    work page 2024

  10. [117]

    A real-time estimation method of carbon emission for cement corporations based on load identification

    Juntao Pan, Zhou Yang, Xingqiu Wei, and Zhenglei Zhou. A real-time estimation method of carbon emission for cement corporations based on load identification. In Journal of Physics: Conference Series, volume 2814, page 012008. IOP Publishing, 2024

    work page 2024

  11. [118]

    Carbon emission prediction models: A review

    Yukai Jin, Ayyoob Sharifi, Zhisheng Li, Sirui Chen, Suzhen Zeng, and Shanlun Zhao. Carbon emission prediction models: A review. Science of the Total Environment, page 172319, 2024

    work page 2024

  12. [2]

    Alshatri and F

    N. Alshatri and F. K. Hussain. Data driven approach of external factors influencing carbon credit prices: A systematic literature review. Social Science Research Network, October 19 2023

    2023

  13. [9]

    Kleimeier and M

    S. Kleimeier and M. Viehs. Carbon disclosure, emission levels, and the cost of debt. Papers.ssrn.com, January 7 2018

    2018

  14. [14]

    Lokuge and S

    N. Lokuge and S. Anders. Carbon-credit systems in agriculture: A review of literature. Papers.ssrn.com, April 1 2022

    2022

  15. [19]

    Yang Liu, Xiaoyan Zhou, Jessica Yang, and Andreas G.F. Hoepner. Corporate carbon emissions and financial performance: Does carbon disclosure mediate the relationship in the uk? Available at SSRN 2941123, 2017

    2017

  16. [23]

    Mandatory corporate carbon disclosure: Evidence from a natural experiment

    Valentin Jouvenot and Philipp Krueger. Mandatory corporate carbon disclosure: Evidence from a natural experiment. Available at SSRN 3434490, 2019

    2019

  17. [104]

    Shakdwipee and L.-E

    M. Shakdwipee and L.-E. Lee. Filling the blanks: Comparing carbon estimates against disclosures. MSCI ESG Research Issue Brief, 2016

    work page 2016

Show all 119 references
  1. [1]

    H. A. Kazem, A. Quteishat, and M. A. Younis. Techno-economical study of solar water pumping system: Optimum design, evaluation, and comparison. Renewable Energy and Environmental Sustainability, 6:41, 2021

    2021

  2. [3]

    Lazarou, C

    S. Lazarou, C. Christodoulou, and V . Vita. Global change assessment model (gcam) considerations of the primary sources energy mix for an energetic scenario that could meet paris agreement. In2019 54th International Universities Power Engineering Conference (UPEC), 2019

    2019

  3. [4]

    Understanding carbon credits

    Gurmit Singh. Understanding carbon credits. Aditya Books Pvt. Ltd., 2009

    2009

  4. [5]

    The global rise of emissions trading

    Climatepolicyinfohub.eu. The global rise of emissions trading. Climate Policy Info Hub. (n.d.)

  5. [6]

    Corporate carbon and financial performance revisited

    Timo Busch, Alexander Bassen, Stefan Lewandowski, and Franziska Sump. Corporate carbon and financial performance revisited. Organization & Environment, 35(1):154–171, 2022

    2022

  6. [7]

    An optimized and interpretable carbon price prediction: Explainable deep learning model

    Gehad Ismail Sayed, Eman I Abd El-Latif, Ashraf Darwish, Vaclav Snasel, and Aboul Ella Hassanien. An optimized and interpretable carbon price prediction: Explainable deep learning model. Chaos, Solitons & Fractals, 188:115533, 2024

    2024

  7. [8]

    Bae Choi, D

    B. Bae Choi, D. Lee, and J. Psaros. An analysis of australian company carbon emission disclosures. Pacific Accounting Review, 25(1):58–79, 2013

    2013

  8. [10]

    A. E. Karim, K. Albitar, and M. Elmarzouky. A novel measure of corporate carbon emission disclosure, the effect of capital expenditures and corporate governance. Journal of Environmental Management, 290:112581, 2021

    2021

  9. [11]

    Kurnia, E

    P. Kurnia, E. Darlis, and A. A. Putra. Carbon emission disclosure, good corporate governance, financial performance, and firm value. The Journal of Asian Finance, Economics and Business, 7(12):223–231, 2020

    2020

  10. [12]

    J. Woo, R. Fatima, C. J. Kibert, R. E. Newman, Y . Tian, and R. S. Srinivasan. Applying blockchain technology for building energy performance measurement, reporting, and verification (mrv) and the carbon credit market: A review of the literature. Building and Environment, 205:...

    2021

  11. [13]

    Trouwloon, C

    D. Trouwloon, C. Streck, T. Chagas, and G. Martinus. Understanding the use of carbon credits by companies: A review of the defining elements of corporate climate claims. Global Challenges, 7(4):2200158, 2023

    2023

  12. [15]

    J. H. Littell, J. Corcoran, and V . Pillai. Systematic Reviews and Meta-Analysis. Oxford University Press, 2008

    2008

  13. [16]

    A guide to systematic literature reviews

    Alison Nightingale. A guide to systematic literature reviews. Surgery (Oxford), 27(9):381–384, 2009

    2009

  14. [17]

    E. M. Matsumura, R. Prakash, and S. C. Vera-Muñoz. Firm-value effects of carbon emissions and carbon disclosures. The Accounting Review, 89(2):695–724, 2014. 24 A PREPRINT - JANUARY 28, 2025

    2014

  15. [18]

    Carbon disclosure and financial performance: Uk environ- mental policy

    Khaled Alsaifi, Marwa Elnahass, and Aly Salama. Carbon disclosure and financial performance: Uk environ- mental policy. Business Strategy and the Environment, 29(2):711–726, 2020

    2020

  16. [20]

    Saka and T

    C. Saka and T. Oshika. Disclosure effects, carbon emissions and corporate value. Sustainability Accounting, Management and Policy Journal, 5(1):22–45, 2014

    2014

  17. [21]

    Y . A. Sudibyo. Carbon emission disclosure: does it matter. In IOP Conference Series: Earth and Environmental Science, volume 106, page 012036, 2018

    2018

  18. [22]

    Firm-value effects of carbon emissions and carbon disclosures—evidence from korea

    Jeong-Hwan Lee and Jin-Hyung Cho. Firm-value effects of carbon emissions and carbon disclosures—evidence from korea. International Journal of Environmental Research and Public Health, 18(22):12166, 2021

    2021

  19. [24]

    The influencing factors of scer price dynamics under the clean development mechanism: theory and econometric analysis

    Chen Zhang, Yaqi Wu, and Yu Yang. The influencing factors of scer price dynamics under the clean development mechanism: theory and econometric analysis. Journal of Systems Science and Complexity, 31:1244–1272, 2018

    2018

  20. [25]

    Scenario-based potential effects of carbon trading in china: An integrated approach

    Cheng Zhang, Qunwei Wang, Dan Shi, Pengfei Li, and Wanhuan Cai. Scenario-based potential effects of carbon trading in china: An integrated approach. Applied Energy, 182:177–190, 2016

    2016

  21. [26]

    Key factors affecting carbon prices from a time-varying perspective

    Ming-Fang Li, Hui Hu, and Lu-Tao Zhao. Key factors affecting carbon prices from a time-varying perspective. Environmental Science and Pollution Research, 29(43):65144–65160, 2022

    2022

  22. [27]

    A multiscale analysis for carbon price drivers

    Bangzhu Zhu, Shunxin Ye, Dong Han, Ping Wang, Kaijian He, Yi-Ming Wei, and Rui Xie. A multiscale analysis for carbon price drivers. Energy Economics, 78:202–216, 2019

    2019

  23. [28]

    An empirical analysis of carbon emission price in china

    Kaile Zhou and Yiwen Li. An empirical analysis of carbon emission price in china. Energy Procedia, 152:823– 828, 2018

    2018

  24. [29]

    Price determination in the eu ets market: Theory and econometric analysis with market fundamentals

    Piia Aatola, Markku Ollikainen, and Anne Toppinen. Price determination in the eu ets market: Theory and econometric analysis with market fundamentals. Energy Economics, 36:380–395, 2013

    2013

  25. [30]

    Price drivers and structural breaks in european carbon prices 2005–2007

    Emilie Alberola, Julien Chevallier, and Benoît Chèze. Price drivers and structural breaks in european carbon prices 2005–2007. Energy Policy, 36(2):787–797, 2008

    2005

  26. [31]

    The determinants of co2 prices in the eu emission trading system

    Yuliya Lovcha, Alejandro Perez-Laborda, and Iryna Sikora. The determinants of co2 prices in the eu emission trading system. Applied Energy, 305:117903, 2022

    2022

  27. [32]

    The long-term trend analysis and scenario simulation of the carbon price based on the energy-economic regulation

    Zhao-Peng Li, Li Yang, Si-Rui Li, and Xiaoling Yuan. The long-term trend analysis and scenario simulation of the carbon price based on the energy-economic regulation. International Journal of Climate Change Strategies and Management, 12(5):653–668, 2020

    2020

  28. [33]

    The impact of the global stock and energy market on eu ets: A structural equation modelling approach

    Zi-Jie Wang and Lu-Tao Zhao. The impact of the global stock and energy market on eu ets: A structural equation modelling approach. Journal of Cleaner Production, 289:125140, 2021

    2021

  29. [34]

    Predicting carbon allowance prices with energy prices: A new approach

    Oluwasegun B Adekoya. Predicting carbon allowance prices with energy prices: A new approach. Journal of Cleaner Production, 282:124519, 2021

    2021

  30. [36]

    Analyzing the drivers of co2 allowance prices in eu ets under the covid-19 pandemic: Considering memd approach with a novel filtering procedure

    Majid Mirzaee Ghazani et al. Analyzing the drivers of co2 allowance prices in eu ets under the covid-19 pandemic: Considering memd approach with a novel filtering procedure. Journal of Cleaner Production, 427:139043, 2023

    2023

  31. [37]

    Time-varying impacts of carbon price drivers in the eu ets: a tvp-var analysis

    Peng Li, Hao Zhang, Yongna Yuan, and Aimin Hao. Time-varying impacts of carbon price drivers in the eu ets: a tvp-var analysis. Frontiers in Environmental Science, 9:651791, 2021

    2021

  32. [38]

    The determinants of carbon emission price—empirical analysis based on the vec model

    Hanyue Guan. The determinants of carbon emission price—empirical analysis based on the vec model. In 2021 2nd International Conference on Big Data Economy and Information Management (BDEIM), pages 68–71. IEEE, 2021

    2021

  33. [39]

    Exploring the short-term and long-term linkages between carbon price and influence factors considering covid-19 impact

    Zhibin Wu, Wen Zhang, and Xiaojun Zeng. Exploring the short-term and long-term linkages between carbon price and influence factors considering covid-19 impact. Environmental Science and Pollution Research , 30(22):61479–61495, 2023

    2023

  34. [40]

    Disentangling the drivers of carbon prices in china’s ets pilots—an eemd approach

    Jia Xu, Xiujie Tan, Gang He, and Yu Liu. Disentangling the drivers of carbon prices in china’s ets pilots—an eemd approach. Technological Forecasting and Social Change, 139:1–9, 2019. 25 A PREPRINT - JANUARY 28, 2025

    2019

  35. [41]

    Study on price fluctuation and influencing factors of regional carbon emission trading in china under the background of high-quality economic development

    Sheng Zeng, Han Zhang, Yuwei Qu, and Boya Zeng. Study on price fluctuation and influencing factors of regional carbon emission trading in china under the background of high-quality economic development. International Energy Journal, 21:201–211, 2021

    2021

  36. [43]

    Exploring volatility of carbon price in european union due to covid-19 pandemic

    Feng Dong, Yujin Gao, Yangfan Li, Jiao Zhu, Mengyue Hu, and Xiaoyun Zhang. Exploring volatility of carbon price in european union due to covid-19 pandemic. Environmental Science and Pollution Research, pages 1–12, 2022

    2022

  37. [44]

    Research on the impact of exchange rates and interest rates on carbon price changes in the context of sustainable development

    Jiajun Yang, Yuzhu Wan, and Songdong Shen. Research on the impact of exchange rates and interest rates on carbon price changes in the context of sustainable development. Frontiers in Ecology and Evolution, 10:1122582, 2023

    2023

  38. [45]

    Influencing factors and fluctuation characteristics of china’s carbon emission trading price

    Kaile Zhou and Yiwen Li. Influencing factors and fluctuation characteristics of china’s carbon emission trading price. Physica A: Statistical Mechanics and its Applications, 524:459–474, 2019

    2019

  39. [46]

    Hyun Seok Kim and Won W. Koo. Factors affecting the carbon allowance market in the us. Energy Policy, 38(4):1879–1884, 2010

    2010

  40. [47]

    Price drivers in the carbon emissions trading scheme: Evidence from chinese emissions trading scheme pilots

    Chang-Jing Ji, Yu-Jie Hu, Bao-Jun Tang, and Shen Qu. Price drivers in the carbon emissions trading scheme: Evidence from chinese emissions trading scheme pilots. Journal of Cleaner Production, 278:123469, 2021

    2021

  41. [48]

    Carbon price prediction under output uncertainty

    Na Liu and Futie Song. Carbon price prediction under output uncertainty. Environmental Science and Pollution Research, 29(15):21577–21590, 2022

    2022

  42. [49]

    Extreme learning machine: theory and applications

    Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew. Extreme learning machine: theory and applications. Neurocomputing, 70(1-3):489–501, 2006

    2006

  43. [50]

    Particle swarm optimization

    James Kennedy and Russell Eberhart. Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, volume 4, pages 1942–1948. IEEE, 1995

    1942

  44. [51]

    Johan A. K. Suykens and Joos Vandewalle. Least squares support vector machine classifiers. Neural Processing Letters, 9:293–300, 1999

    1999

  45. [52]

    Sun and Y

    W. Sun and Y . Wang. Factor analysis and carbon price prediction based on empirical mode decomposition and least squares support vector machine optimized by improved particle swarm optimization. Carbon Management, pages 1–15, 2020

    2020

  46. [53]

    Torres, Marcelo A

    María E. Torres, Marcelo A. Colominas, Gaston Schlotthauer, and Patrick Flandrin. A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4144–4147. IEEE, 2011

    2011

  47. [54]

    Nadirgil

    O. Nadirgil. Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. Journal of Environmental Management, 342:118061, 2023

    2023

  48. [55]

    Colominas, Gastón Schlotthauer, and María E

    Marcelo A. Colominas, Gastón Schlotthauer, and María E. Torres. Improved complete ensemble emd: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, 14:19–29, 2014

    2014

  49. [56]

    Hao and C

    Y . Hao and C. Tian. A hybrid framework for carbon trading price forecasting: The role of multiple influence factor. Journal of Cleaner Production, 262:120378, 2020

    2020

  50. [57]

    The wavelet transform, time-frequency localization and signal analysis

    Ingrid Daubechies. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5):961–1005, 1990

    1990

  51. [58]

    Support-vector networks

    Corinna Cortes. Support-vector networks. Machine Learning, 1995

    1995

  52. [59]

    Li and D

    J. Li and D. Liu. Carbon price forecasting based on secondary decomposition and feature screening. Energy, 278:127783, 2023

    2023

  53. [60]

    A hybrid vmd–bigru model for rubber futures time series forecasting

    Qing Zhu, Fan Zhang, Shan Liu, Yiqiong Wu, and Lin Wang. A hybrid vmd–bigru model for rubber futures time series forecasting. Applied Soft Computing, 84:105739, 2019

    2019

  54. [61]

    Gaussian process regression for numerical wind speed prediction enhancement

    Haoshu Cai, Xiaodong Jia, Jianshe Feng, Wenzhe Li, Yuan-Ming Hsu, and Jay Lee. Gaussian process regression for numerical wind speed prediction enhancement. Renewable Energy, 146:2112–2123, 2020

    2020

  55. [62]

    J. Wang, Q. Cui, and X. Sun. A novel framework for carbon price prediction using comprehensive feature screening, bidirectional gate recurrent unit and gaussian process regression. Journal of Cleaner Production, 314:128024, 2021

    2021

  56. [63]

    Empirical wavelet transform

    Jerome Gilles. Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16):3999–4010, 2013. 26 A PREPRINT - JANUARY 28, 2025

    2013

  57. [64]

    Bezdek and Joseph C

    James C. Bezdek and Joseph C. Dunn. Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions. IEEE Transactions on Computers, 100(8):835–838, 1975

    1975

  58. [65]

    Liu and L

    H. Liu and L. Shen. Forecasting carbon price using empirical wavelet transform and gated recurrent unit neural network. Carbon Management, 11(1):25–37, 2019

    2019

  59. [66]

    Kidney-inspired algorithm for optimization problems

    Najmeh Sadat Jaddi, Jafar Alvankarian, and Salwani Abdullah. Kidney-inspired algorithm for optimization problems. Communications in Nonlinear Science and Numerical Simulation, 42:358–369, 2017

    2017

  60. [67]

    Huang and Z

    Y . Huang and Z. He. Carbon price forecasting with optimization prediction method based on unstructured combination. Science of the Total Environment, 725:138350, 2020

    2020

  61. [68]

    Long short-term memory

    S Hochreiter. Long short-term memory. Neural Computation MIT-Press, 1997

    1997

  62. [69]

    Ensemble empirical mode decomposition: a noise-assisted data analysis method

    Zhaohua Wu and Norden E Huang. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01):1–41, 2009

    2009

  63. [70]

    Y . Min, Z. Shuzhen, and L. Wuwei. Carbon price prediction based on multi-factor meemd-lstm model.Heliyon, 8(12), 2022

    2022

  64. [71]

    Xgboost: A scalable tree boosting system

    Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016

    2016

  65. [72]

    Random forests

    Leo Breiman. Random forests. Machine learning, 45:5–32, 2001

    2001

  66. [73]

    Predicting the oil production using the novel multivariate nonlinear model based on arps decline model and kernel method

    Xin Ma and Zhibin Liu. Predicting the oil production using the novel multivariate nonlinear model based on arps decline model and kernel method. Neural Computing and Applications, 29:579–591, 2018

    2018

  67. [74]

    Modelling the nonlinear relationship between co2 emissions and economic growth using a pso algorithm-based grey verhulst model

    Zheng-Xin Wang and Qin Li. Modelling the nonlinear relationship between co2 emissions and economic growth using a pso algorithm-based grey verhulst model. Journal of Cleaner Production, 207:214–224, 2019

    2019

  68. [75]

    Classification of audio signals using svm and rbfnn

    P Dhanalakshmi, S Palanivel, and Vennila Ramalingam. Classification of audio signals using svm and rbfnn. Expert systems with applications, 36(3):6069–6075, 2009

    2009

  69. [76]

    H. Lu, X. Ma, K. Huang, and M. Azimi. Carbon trading volume and price forecasting in china using multiple machine learning models. Journal of Cleaner Production, 249:119386, 2020

    2020

  70. [77]

    Regularized extreme learning machine

    Wanyu Deng, Qinghua Zheng, and Lin Chen. Regularized extreme learning machine. In 2009 IEEE symposium on computational intelligence and data mining, pages 389–395. IEEE, 2009

    2009

  71. [78]

    Grasshopper optimization algorithm for multi-objective optimization problems

    Seyedeh Zahra Mirjalili, Seyedali Mirjalili, Shahrzad Saremi, Hossam Faris, and Ibrahim Aljarah. Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48:805–820, 2018

    2018

  72. [79]

    Y . Hao, C. Tian, and C. Wu. Modelling of carbon price in two real carbon trading markets.Journal of Cleaner Production, 244:118556, 2020

    2020

  73. [80]

    The whale optimization algorithm

    Seyedali Mirjalili and Andrew Lewis. The whale optimization algorithm. Advances in engineering software, 95:51–67, 2016

    2016

  74. [81]

    Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine

    Cheng Lian, Zhigang Zeng, Wei Yao, and Huiming Tang. Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Natural hazards, 66:759–771, 2013

    2013

  75. [82]

    S. Yang, D. Chen, S. Li, and W. Wang. Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm. Science of the Total Environment, 716:137117, 2020

    2020

  76. [83]

    Postwar us business cycles: an empirical investigation

    Robert J Hodrick and Edward C Prescott. Postwar us business cycles: an empirical investigation. Journal of Money, credit, and Banking, pages 1–16, 1997

    1997

  77. [84]

    L. T. Zhao, J. Miao, S. Qu, and X. H. Chen. A multi-factor integrated model for carbon price forecasting: market interaction promoting carbon emission reduction. Science of The Total Environment, 796:149110, 2021

    2021

  78. [85]

    Symbolic transfer entropy

    Matthäus Staniek and Klaus Lehnertz. Symbolic transfer entropy. Physical review letters, 100(15):158101, 2008

    2008

  79. [87]

    Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

    Hanchuan Peng, Fuhui Long, and Chris Ding. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8):1226–1238, 2005

    2005

  80. [88]

    A novel cryptocurrency price trend forecasting model based on lightgbm

    Xiaolei Sun, Mingxi Liu, and Zeqian Sima. A novel cryptocurrency price trend forecasting model based on lightgbm. Finance Research Letters, 32:101084, 2020. 27 A PREPRINT - JANUARY 28, 2025

    2020

  81. [89]

    Avei-bo: an efficient bayesian optimization using adaptively varied expected improvement

    Cheng Yan, Han Du, Enzi Kang, Dong Mi, He Liu, and Yancheng You. Avei-bo: an efficient bayesian optimization using adaptively varied expected improvement. Structural and Multidisciplinary Optimization, 65(6):164, 2022

    2022

  82. [91]

    Structured pruning of deep convolutional neural networks

    Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung. Structured pruning of deep convolutional neural networks. ACM Journal on Emerging Technologies in Computing Systems (JETC), 13(3):1–18, 2017

    2017

  83. [92]

    Time series forecasting of petroleum production using deep lstm recurrent networks

    Alaa Sagheer and Mostafa Kotb. Time series forecasting of petroleum production using deep lstm recurrent networks. Neurocomputing, 323:203–213, 2019

    2019

  84. [94]

    Data multi-scale decomposition strategies for air pollution forecasting: a comprehensive review

    Hui Liu, Shi Yin, Chao Chen, and Zhu Duan. Data multi-scale decomposition strategies for air pollution forecasting: a comprehensive review. Journal of Cleaner Production, 277:124023, 2020

    2020

  85. [95]

    Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms

    Kefei Zhang, Xiaolin Yang, Teng Wang, Jesse Thé, Zhongchao Tan, and Hesheng Yu. Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms. Journal of Cleaner Production, 405:136959, 2023

    2023

  86. [96]

    Characterization of surface emg signal based on fuzzy entropy

    Weiting Chen, Zhizhong Wang, Hongbo Xie, and Wangxin Yu. Characterization of surface emg signal based on fuzzy entropy. IEEE Transactions on neural systems and rehabilitation engineering, 15(2):266–272, 2007

    2007

  87. [97]

    Variational mode decomposition

    Konstantin Dragomiretskiy and Dominique Zosso. Variational mode decomposition. IEEE transactions on signal processing, 62(3):531–544, 2013

    2013

  88. [98]

    Cuckoo search via lévy flights

    Xin-She Yang and Suash Deb. Cuckoo search via lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC), pages 210–214. Ieee, 2009

    2009

  89. [99]

    An enhanced decomposition integration model for deterministic and probabilistic carbon price prediction based on two-stage feature extraction and intelligent weight optimization

    Xin Zhang and Jujie Wang. An enhanced decomposition integration model for deterministic and probabilistic carbon price prediction based on two-stage feature extraction and intelligent weight optimization. Journal of Cleaner Production, 415:137791, 2023

    2023

  90. [100]

    Deep transformer models for time series forecasting: The influenza prevalence case

    Neo Wu, Bradley Green, Xue Ben, and Shawn O’Banion. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317, 2020

    2001 arXiv

  91. [101]

    Hybrid carbon price forecasting using a deep augmented fedformer model and multimodel optimization piecewise error correction

    Jun-Tao Hong, Yu-Long Bai, Yu-Ting Huang, and Zhi-Ru Chen. Hybrid carbon price forecasting using a deep augmented fedformer model and multimodel optimization piecewise error correction. Expert Systems with Applications, 247:123325, 2024

    2024

  92. [102]

    G. S. Atsalakis. Using computational intelligence to forecast carbon prices. Applied Soft Computing, 43:107–116, 2016

    2016

  93. [103]

    Goldhammer, C

    B. Goldhammer, C. Busse, and T. Busch. Estimating corporate carbon footprints with externally available data. Journal of Industrial Ecology, 21(5):1165–1179, 2016

    2016

  94. [105]

    Earnings, book values, and dividends in equity valuation: An empirical perspective

    James A Ohlson. Earnings, book values, and dividends in equity valuation: An empirical perspective. Contempo- rary accounting research, 18(1):107–120, 2001

    2001

  95. [106]

    P. A. Griffin, D. H. Lont, and E. Y . Sun. The relevance to investors of greenhouse gas emission disclosures. Contemporary Accounting Research, 34(2):1265–1297, 2017

    2017

  96. [107]

    A carbon risk prediction model for chinese heavy-polluting industrial enterprises based on support vector machine

    Zhifang Zhou, Tian Xiao, Xiaohong Chen, and Chang Wang. A carbon risk prediction model for chinese heavy-polluting industrial enterprises based on support vector machine. Chaos, Solitons & Fractals, 89:304–315, 2016

    2016

  97. [108]

    Nguyen, I

    Q. Nguyen, I. Diaz-Rainey, and D. Kuruppuarachchi. Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach. Energy Economics, page 105129, 2021

    2021

  98. [109]

    Principal component analysis

    Svante Wold, Kim Esbensen, and Paul Geladi. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37–52, 1987

    1987

  99. [110]

    Enterprise carbon emission prediction algorithm based on multidimensional characteristic data

    Xiong Li, Ling Zhou, Yuan Ji, Siwu Yu, and Jun Zou. Enterprise carbon emission prediction algorithm based on multidimensional characteristic data. In International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), volume 12700, pages 15–22. SPI...

    2023

  100. [111]

    A fast transfer- based adaptive recurrent neural network model for enterprise carbon emission prediction

    Yun Chen, Zhijun Pan, Xiaohui Wang, Chaoran Fu, Yiwen Cai, Jiahang Jiang, and Haizhou Du. A fast transfer- based adaptive recurrent neural network model for enterprise carbon emission prediction. In 2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT), pages...

    2023

  101. [116]

    Bidirectional long short-term memory networks for relation classification

    Shu Zhang, Dequan Zheng, Xinchen Hu, and Ming Yang. Bidirectional long short-term memory networks for relation classification. In Proceedings of the 29th Pacific Asia conference on language, information and computation, pages 73–78, 2015

    2015

  102. [119]

    Firms and climate change: a review of carbon risk in corporate finance

    Yao Wang, Zhenshu Wu, and Guangxiao Zhang. Firms and climate change: a review of carbon risk in corporate finance. Carbon Neutrality, 1(1):6, 2022. 29

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.