Forecasting Carbon Price Volatility in China's Ets a Policy-augmented Deep Learning Frame-work Inte-grating Innovation-driven Policies and Green Investment Thresholds

Forecasting Carbon Price Volatility in China's Ets a Policy-augmented Deep Learning Frame-work Inte-grating Innovation-driven Policies and Green Investment Thresholds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Forecasting Carbon Price Volatility in China's Ets a Policy-augmented Deep Learning Frame-work Inte-grating Innovation-driven Policies and Green Investment Thresholds Sichen Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8326151/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The stability of carbon pricing mechanisms is fundamental to achieving global climate targets, yet carbon price volatility in emerging markets like China's Emissions Trading Scheme (ETS) poses significant challenges to market efficiency and investment certainty. Existing forecasting models predominantly focus on statistical patterns while neglecting the profound influence of policy interventions and firm-level behavioral dynamics. This study addresses this critical gap by developing a comprehensive analytical framework that integrates causal inference with advanced machine learning to both explain and pre-dict carbon price volatility. We make four primary contributions. First, grounded in sig-naling theory, we construct a dynamic Innovation-Driven Policy (IDP) Index through sys-tematic textual analysis of 5,820 government policy documents spanning 2013–2023, quantifying the intensity and consistency of green innovation signals. Second, drawing from real options theory, we empirically identify a statistically significant Green Invest-ment Threshold (GIT) at 28.5 billion RMB monthly green bond issuance using Hansen's threshold regression model, revealing distinct market volatility regimes. Third, we devel-op a novel hybrid forecasting architecture, the VMD-Attention-GRU (VAG) model, which synergistically combines Variational Mode Decomposition (VMD) for noise reduction, an attention mechanism for dynamic feature weighting, and Gated Recurrent Units (GRU) optimized via the Sparrow Search Algorithm (SSA). Fourth, we employ SHAP (SHapley Additive exPlanations) analysis to achieve unprecedented model interpretability, quanti-fying the marginal contribution of each factor to volatility forecasts. Utilizing comprehen-sive data from China's pilot and national ETS markets (2014–2023, N = 2,452), our causal analysis demonstrates that innovation-driven policies significantly dampen long-term volatility (cumulative abnormal volatility reduction of 8.5% following major policy an-nouncements, p < 0.05), while the identified investment threshold creates a structural break in volatility persistence (autoregressive coefficient drops from 0.45 to 0.28 above threshold). The VAG model achieves superior forecasting performance, reducing RMSE by 27.8% compared to standalone GRU and by 14.3% compared to VMD-SSA-GRU without policy variables (test set RMSE = 0.0078, MAPE = 17.2%). SHAP analysis confirms that policy sig-nals and investment regime indicators rank as the second and third most influential pre-dictors after market memory effects, validating our theoretical framework. These findings provide actionable insights for policymakers to design stabilizing interventions and for market participants to navigate the complexities of carbon markets in the context of global sustainability transitions. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Business and commerce/Finance Social science/Finance Physical sciences/Mathematics and computing Carbon Price Volatility Emissions Trading Scheme Causal Inference Green Investment Threshold Policy Textual Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8326151","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562430014,"identity":"fd2aa621-5d5b-4a19-a04a-c2f12aca7a4c","order_by":0,"name":"Sichen 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Thresholds\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Carbon Price Volatility, Emissions Trading Scheme, Causal Inference, Green Investment Threshold, Policy Textual Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8326151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8326151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe stability of carbon pricing mechanisms is fundamental to achieving global climate targets, yet carbon price volatility in emerging markets like China's Emissions Trading Scheme (ETS) poses significant challenges to market efficiency and investment certainty. Existing forecasting models predominantly focus on statistical patterns while neglecting the profound influence of policy interventions and firm-level behavioral dynamics. This study addresses this critical gap by developing a comprehensive analytical framework that integrates causal inference with advanced machine learning to both explain and pre-dict carbon price volatility. We make four primary contributions. First, grounded in sig-naling theory, we construct a dynamic Innovation-Driven Policy (IDP) Index through sys-tematic textual analysis of 5,820 government policy documents spanning 2013\u0026ndash;2023, quantifying the intensity and consistency of green innovation signals. Second, drawing from real options theory, we empirically identify a statistically significant Green Invest-ment Threshold (GIT) at 28.5\u0026nbsp;billion RMB monthly green bond issuance using Hansen's threshold regression model, revealing distinct market volatility regimes. Third, we devel-op a novel hybrid forecasting architecture, the VMD-Attention-GRU (VAG) model, which synergistically combines Variational Mode Decomposition (VMD) for noise reduction, an attention mechanism for dynamic feature weighting, and Gated Recurrent Units (GRU) optimized via the Sparrow Search Algorithm (SSA). Fourth, we employ SHAP (SHapley Additive exPlanations) analysis to achieve unprecedented model interpretability, quanti-fying the marginal contribution of each factor to volatility forecasts. Utilizing comprehen-sive data from China's pilot and national ETS markets (2014\u0026ndash;2023, N\u0026thinsp;=\u0026thinsp;2,452), our causal analysis demonstrates that innovation-driven policies significantly dampen long-term volatility (cumulative abnormal volatility reduction of 8.5% following major policy an-nouncements, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the identified investment threshold creates a structural break in volatility persistence (autoregressive coefficient drops from 0.45 to 0.28 above threshold). The VAG model achieves superior forecasting performance, reducing RMSE by 27.8% compared to standalone GRU and by 14.3% compared to VMD-SSA-GRU without policy variables (test set RMSE\u0026thinsp;=\u0026thinsp;0.0078, MAPE\u0026thinsp;=\u0026thinsp;17.2%). SHAP analysis confirms that policy sig-nals and investment regime indicators rank as the second and third most influential pre-dictors after market memory effects, validating our theoretical framework. These findings provide actionable insights for policymakers to design stabilizing interventions and for market participants to navigate the complexities of carbon markets in the context of global sustainability transitions.\u003c/p\u003e","manuscriptTitle":"Forecasting Carbon Price Volatility in China's Ets a Policy-augmented Deep Learning Frame-work Inte-grating Innovation-driven Policies and Green Investment Thresholds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-20 02:14:34","doi":"10.21203/rs.3.rs-8326151/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"046f184e-f987-4f6b-970b-d95463c2dfd3","owner":[],"postedDate":"December 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59983062,"name":"Business and commerce/Economics"},{"id":59983063,"name":"Social science/Economics"},{"id":59983064,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":59983065,"name":"Business and commerce/Finance"},{"id":59983066,"name":"Social science/Finance"},{"id":59983067,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-20T04:54:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-20 02:14:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8326151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8326151","identity":"rs-8326151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}