{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:58:11Z","timestamp":1773662291671,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Provincial Natural Science Foundation Youth Project","award":["ZR2025QC764"],"award-info":[{"award-number":["ZR2025QC764"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Achieving simultaneous control over total carbon emissions and intensity is essential for China\u2019s dual carbon goals. Using panel data from 1235 listed manufacturing firms (2015\u20132022), we construct a composite index to measure dual carbon control and investigate how data elements influence corporate carbon performance from an industry heterogeneity perspective. The main findings are as follows. (1) Data elements significantly enhance dual carbon control, with effects concentrated in high-pollution sectors, particularly metallurgy and mineral products, while remaining insignificant in low-pollution industries. (2) Mechanisms differ across industry types: capacity utilization drives improvements in high-pollution industries, whereas green technology innovation matters in low-pollution sectors such as agro-processing and textiles. (3) ESG disclosure and green credit subsidies amplify these effects, though with varying efficacy. Policymakers should adopt differentiated strategies including removing structural barriers to green innovation in high-pollution industries and activating capacity utilization through monitoring standards and technology markets in low-pollution sectors. A tailored policy framework is essential to realize the full potential of data elements in advancing China\u2019s dual carbon goals.<\/jats:p>","DOI":"10.3390\/systems14030305","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:10:43Z","timestamp":1773655843000},"page":"305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Elements and the Dual Control of Carbon Emissions: A Perspective Based on Industry Differences"],"prefix":"10.3390","volume":"14","author":[{"given":"Na","family":"Liu","sequence":"first","affiliation":[{"name":"Business School, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Su","sequence":"additional","affiliation":[{"name":"School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122567","DOI":"10.1016\/j.techfore.2023.122567","article-title":"Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises","volume":"192","author":"Gao","year":"2023","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1038\/s41467-024-53956-1","article-title":"Artificial Intelligence (AI)-driven approach to climate action and sustainable development","volume":"16","author":"Cho","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"117755","DOI":"10.1016\/j.jenvman.2023.117755","article-title":"Synergistic effect of pollution reduction and carbon emission mitigation in the digital economy","volume":"337","author":"Hu","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107143","DOI":"10.1016\/j.eiar.2023.107143","article-title":"Does digital dividend matter in China\u2019s green low-carbon development: Environmental impact assessment of the big data comprehensive pilot zones policy","volume":"101","author":"Wei","year":"2023","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1002\/sd.3112","article-title":"Sustainable development through digital innovation: Unveiling the impact of big data comprehensive experimental zones on energy utilization efficiency","volume":"33","author":"Xue","year":"2025","journal-title":"Sustain. Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1057\/s41599-025-04805-z","article-title":"Mechanism conflicts: Carbon reduction pathways and optimization in China\u2019s Big Data Policy","volume":"12","author":"Zhou","year":"2025","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102165","DOI":"10.1016\/j.ecoinf.2023.102165","article-title":"Artificial intelligence for carbon emissions using system of systems theory","volume":"76","author":"Gaur","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1057\/s41599-024-03223-x","article-title":"How does the digital transformation of agriculture affect carbon emissions? Evidence from China\u2019s provincial panel data","volume":"11","author":"Chen","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.iref.2023.02.019","article-title":"Does enterprise digital transformation contribute to the carbon emission reduction? Micro-level evidence from China","volume":"86","author":"Shang","year":"2023","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109246","DOI":"10.1016\/j.ijpe.2024.109246","article-title":"Is it possible for semiconductor companies to reduce carbon emissions through digital transformation? Evidence from China","volume":"272","author":"Zheng","year":"2024","journal-title":"Int. J. Prod. Econ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"158401","DOI":"10.1016\/j.scitotenv.2022.158401","article-title":"How does digital economy affect carbon emissions? Evidence from global 60 countries","volume":"852","author":"Dong","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"116632","DOI":"10.1016\/j.jenvman.2022.116632","article-title":"Digital inclusive finance and consumption-based embodied carbon emissions: A dual perspective of consumption and industry upgrading","volume":"325","author":"Zhang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3416","DOI":"10.1038\/s41598-025-87098-1","article-title":"Research on the impact of the digital economy on carbon emissions based on the dual perspectives of carbon emission reduction and carbon efficiency","volume":"15","author":"Liu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112927","DOI":"10.1016\/j.enpol.2022.112927","article-title":"Digital economy and carbon emission performance: Evidence at China\u2019s city level","volume":"165","author":"Zhang","year":"2022","journal-title":"Energy Policy"},{"key":"ref_15","first-page":"101358","article-title":"Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies","volume":"47","author":"Chen","year":"2021","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"119493","DOI":"10.1016\/j.jenvman.2023.119493","article-title":"Transition towards dual control of CO2 emissions and intensity through supply chain management in China","volume":"348","author":"Wang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mhlanga, D. (2023). Artificial intelligence and machine learning for energy consumption and production in emerging markets: A review. Energies, 16.","DOI":"10.3390\/en16020745"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s10311-023-01604-3","article-title":"Artificial intelligence for waste management in smart cities: A review","volume":"21","author":"Fang","year":"2023","journal-title":"Environ. Chem. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5916","DOI":"10.1038\/s41467-024-50088-4","article-title":"Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale","volume":"15","author":"Ding","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101911","DOI":"10.1016\/j.techsoc.2022.101911","article-title":"How digital industries affect China\u2019s carbon emissions? Analysis of the direct and indirect structural effects","volume":"68","author":"Wang","year":"2022","journal-title":"Technol. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"158403","DOI":"10.1016\/j.scitotenv.2022.158403","article-title":"Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China","volume":"852","author":"Zhang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"19294","DOI":"10.1007\/s13132-024-01895-z","article-title":"How digital technology reduces carbon emissions: From the perspective of green innovation, industry upgrading, and energy transition","volume":"15","author":"Huang","year":"2024","journal-title":"J. Knowl. Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.spc.2022.11.022","article-title":"Does digital infrastructure cut carbon emissions in Chinese cities?","volume":"35","author":"Tang","year":"2023","journal-title":"Sustain. Prod. Consum."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"131570","DOI":"10.1016\/j.jclepro.2022.131570","article-title":"The dynamic impact of digital economy on carbon emission reduction: Evidence city-level empirical data in China","volume":"351","author":"Li","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107203","DOI":"10.1016\/j.eiar.2023.107203","article-title":"How does digitalization drive carbon emissions? The inverted U-shaped effect in China","volume":"102","author":"Zheng","year":"2023","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106762","DOI":"10.1016\/j.resconrec.2022.106762","article-title":"The impact of the urban digital economy on China\u2019s carbon intensity: Spatial spillover and mediating effect","volume":"189","author":"Cheng","year":"2023","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106445","DOI":"10.1016\/j.resconrec.2022.106445","article-title":"The impact of digital technology development on carbon emissions: A spatial effect analysis for China","volume":"185","author":"Liu","year":"2022","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3686","DOI":"10.1016\/j.egyr.2025.03.018","article-title":"Enhancing energy conservation and carbon emission reduction synergies through digital technology: Firm-level evidence from China","volume":"13","author":"Ma","year":"2025","journal-title":"Energy Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106772","DOI":"10.1016\/j.eneco.2023.106772","article-title":"A pathway to sustainable development: Digitization and green productivity","volume":"124","author":"Lee","year":"2023","journal-title":"Energy Econ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120808","DOI":"10.1016\/j.techfore.2021.120808","article-title":"The role of big data analytics capabilities in greening e-procurement: A higher order PLS-SEM analysis","volume":"169","author":"AlNuaimi","year":"2021","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"123124","DOI":"10.1016\/j.techfore.2023.123124","article-title":"Technology-driven carbon reduction: Analyzing the impact of digital technology on China\u2019s carbon emission and its mechanism","volume":"200","author":"Liu","year":"2024","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"060809","DOI":"10.1115\/1.4062545","article-title":"Carbon neutrality: A review","volume":"23","author":"He","year":"2023","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24300","DOI":"10.1109\/JIOT.2025.3554791","article-title":"Greening Edge AI: Optimizing Inference Accuracy and Reducing Carbon Emissions with Renewable Energy","volume":"12","author":"Ma","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1002\/sd.3222","article-title":"Artificial intelligence-driven sustainability: Enhancing carbon capture for sustainable development goals\u2013A review","volume":"33","author":"Manikandan","year":"2025","journal-title":"Sustain. Dev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1038\/s41558-022-01377-7","article-title":"Aligning artificial intelligence with climate change mitigation","volume":"12","author":"Kaack","year":"2022","journal-title":"Nat. Clim. Change"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"948","DOI":"10.54660\/.IJMRGE.2023.4.1.948-960","article-title":"AI-driven predictive analytics for carbon emission reduction in industrial manufacturing: A machine learning approach to sustainable production","volume":"4","author":"Ojadi","year":"2023","journal-title":"Int. J. Multidiscip. Res. Growth Eval."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s10311-024-01799-z","article-title":"Artificial intelligence for calculating and predicting building carbon emissions: A review","volume":"23","author":"Hua","year":"2025","journal-title":"Environ. Chem. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"14690","DOI":"10.1109\/ACCESS.2024.3351468","article-title":"The optimization of carbon emission prediction in low carbon energy economy under big data","volume":"12","author":"Luo","year":"2024","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.techfore.2018.07.055","article-title":"Technological challenges of green innovation and sustainable resource management with large scale data","volume":"144","author":"Song","year":"2019","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100384","DOI":"10.1016\/j.jik.2023.100384","article-title":"Intelligent manufacturing, green technological innovation and environmental pollution","volume":"8","author":"Shen","year":"2023","journal-title":"J. Innov. Knowl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11963","DOI":"10.1038\/s41598-025-96834-6","article-title":"Spatial\u2013temporal evolution, drivers, and pathways of the synergistic effects of digital transformation on pollution and carbon reduction in heavily polluting enterprises","volume":"15","author":"Mai","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"121579","DOI":"10.1016\/j.jenvman.2024.121579","article-title":"Blessing or curse? The role of digital technology innovation in carbon emission efficiency","volume":"365","author":"Li","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.enpol.2022.113338","article-title":"Green technology innovation and CO2 emission in China: Evidence from a spatial-temporal analysis and a nonlinear Spatial Durbin Model","volume":"172","author":"Chen","year":"2023","journal-title":"Energy Policy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2022.06.091","article-title":"Measuring the effects of green technology innovations and renewable energy investment for reducing carbon emissions in China","volume":"197","author":"Kuang","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1016\/j.renene.2022.05.084","article-title":"Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions?","volume":"193","author":"Habiba","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_46","first-page":"34","article-title":"From \u201cGreen Paradox\u201d to \u201cDual Synergy\u201d: Intelligent Transformation and Enterprise Carbon Emissions","volume":"35","author":"Song","year":"2025","journal-title":"China Popul. Resour. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.eap.2023.07.007","article-title":"Effect of digital economy on air pollution in China? New evidence from the \u201cNational Big Data Comprehensive Pilot Area\u201d policy","volume":"79","author":"Zhang","year":"2023","journal-title":"Econ. Anal. Policy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"116524","DOI":"10.1016\/j.jenvman.2022.116524","article-title":"How does digital technology promote carbon emission reduction? Empirical evidence based on e-commerce pilot city policy in China","volume":"325","author":"Wang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_49","first-page":"49","article-title":"Common Institutional Ownership and Enterprise Capacity Utilization","volume":"48","author":"Du","year":"2022","journal-title":"Financ. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"121987","DOI":"10.1016\/j.jenvman.2024.121987","article-title":"The influence of digital intelligence transformation on carbon emission reduction in manufacturing firms","volume":"367","author":"Cao","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"119313","DOI":"10.1016\/j.envres.2024.119313","article-title":"Does digital-industrial technology integration reduce corporate carbon emissions?","volume":"257","author":"Li","year":"2024","journal-title":"Environ. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101218","DOI":"10.1016\/j.esr.2023.101218","article-title":"The digital economy, industrial structure upgrading, and carbon emission intensity\u2014\u2014Empirical evidence from China\u2019s provinces","volume":"50","author":"Chang","year":"2023","journal-title":"Energy Strategy Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1002\/sd.2175","article-title":"Theoretical framework for the carbon emissions effects of technological progress and renewable energy consumption","volume":"29","author":"Hasanov","year":"2021","journal-title":"Sustain. Dev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108343","DOI":"10.1016\/j.eiar.2026.108343","article-title":"An analytical framework of quantifying carbon emission impacts of construction and demolition waste circularity and trading: A case study of the UK","volume":"118","author":"Ou","year":"2026","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"107085","DOI":"10.1016\/j.eneco.2023.107085","article-title":"Seeing green: How does digital infrastructure affect carbon emission intensity?","volume":"127","author":"Zhang","year":"2023","journal-title":"Energy Econ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119329","DOI":"10.1016\/j.apenergy.2022.119329","article-title":"Impacts of digital technology on energy sustainability: China case study","volume":"323","author":"Wang","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101002","DOI":"10.1016\/j.seps.2020.101002","article-title":"The effect of artificial intelligence on carbon intensity: Evidence from China\u2019s industrial sector","volume":"83","author":"Liu","year":"2022","journal-title":"Socio-Econ. Plan. Sci."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/305\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:00:57Z","timestamp":1773658857000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,15]]},"references-count":57,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["systems14030305"],"URL":"https:\/\/doi.org\/10.3390\/systems14030305","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,15]]}}}