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 <front>
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 <journal-id journal-id-type="publisher-id">dongu-nec.ru</journal-id>
 <journal-title-group>
 <journal-title xml:lang="ru">Новое в экономической кибернетике</journal-title>
 <trans-title-group xml:lang="en">
 <trans-title>New in Economic Cybernetics</trans-title>
 </trans-title-group>
 </journal-title-group>
 <issn publication-format="electronic">2523-448X</issn>
 </journal-meta>
 <article-meta>
 <article-id pub-id-type="publisher-id">533</article-id>
 <article-id pub-id-type="doi">10.5281/zenodo.20970723</article-id>
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 <subj-group subj-group-type="toc-heading" xml:lang="en">
 <subject>Articles</subject>
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 <subject>Статьи</subject>
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 <subj-group subj-group-type="article-type">
 <subject>Research Article</subject>
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 </article-categories>
 <title-group>
 <article-title xml:lang="en">FIRM-LEVEL CORRELATES OF LOW-CARBON INNOVATION IN CHINESE AGRIBUSINESS FIRMS</article-title>
 <trans-title-group xml:lang="ru">
 <trans-title>КОРРЕЛЯЦИИ НИЗКОУГЛЕРОДНЫХ ИННОВАЦИЙ НА УРОВНЕ КОМПАНИЙ В КИТАЙСКИХ АГРОФИРМАХ</trans-title>
 </trans-title-group>
 </title-group>
 <contrib-group>
 <contrib contrib-type="author">
 <contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8585-0837</contrib-id>
 <name-alternatives>
 <name xml:lang="en">
 <surname>Tarasyev</surname>
 <given-names>Alexander</given-names>
 </name>
 <name xml:lang="ru">
 <surname>Тарасьев</surname>
 <given-names>Александр Александрович</given-names>
 </name>
 </name-alternatives>
 <email>a.a.tarasyev@urfu.ru</email>
 <xref ref-type="aff" rid="aff1">1</xref>
 </contrib>
 <contrib contrib-type="author">
 <contrib-id contrib-id-type="orcid">https://orcid.org/0009-0005-9690-3953</contrib-id>
 <name-alternatives>
 <name xml:lang="en">
 <surname>Zhu</surname>
 <given-names>Weijun</given-names>
 </name>
 <name xml:lang="ru">
 <surname>Чжу</surname>
 <given-names>Вэйцзюнь</given-names>
 </name>
 </name-alternatives>
 <email>vchzhu@urfu.ru</email>
 <xref ref-type="aff" rid="aff2">2</xref>
 </contrib>
 </contrib-group>
 <aff-alternatives id="aff1">
 <aff xml:lang="ru">
 <institution>Уральский федеральный университет имени первого президента России Б.Н. Ельцина</institution>
 </aff>
 <aff xml:lang="en">
 <institution>Ural Federal University named after the first President of Russia B.N. Yeltsin</institution>
 </aff>
 </aff-alternatives>
 <aff-alternatives id="aff2">
 <aff xml:lang="ru">
 <institution>Уральский федеральный университет имени первого президента России Б.Н. Ельцина</institution>
 </aff>
 <aff xml:lang="en">
 <institution>Ural Federal University named after the first President of Russia B.N. Yeltsin</institution>
 </aff>
 </aff-alternatives>
 <pub-date date-type="pub" iso-8601-date="2026-07-01" publication-format="electronic">
 <day>01</day>
 <month>07</month>
 <year>2026</year>
 </pub-date>
 <issue>2</issue>
 <issue-title xml:lang="en">NO2 (2026)</issue-title>
 <issue-title xml:lang="ru">№2 (2026)</issue-title>
 <fpage>107</fpage>
 <lpage>122</lpage>
 <history>
 <date date-type="received" iso-8601-date="2026-07-07">
 <day>07</day>
 <month>07</month>
 <year>2026</year>
 </date>
 </history>
 <permissions>
 <copyright-statement xml:lang="ru">Copyright ©; 2026, Новое в экономической кибернетике</copyright-statement>
 <copyright-statement xml:lang="en">Copyright ©; 2026, New in Economic Cybernetics</copyright-statement>
 <copyright-year>2026</copyright-year>
 <copyright-holder xml:lang="ru">Новое в экономической кибернетике</copyright-holder>
 <copyright-holder xml:lang="en">New in Economic Cybernetics</copyright-holder>
 <license license-type="open-access" ns0:href="https://creativecommons.org/licenses/by-nc/4.0/" xml:lang="ru">
 <license-p>Эта статья распространяется на условиях лицензии Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)</license-p>
 </license>
 <license license-type="open-access" ns0:href="https://creativecommons.org/licenses/by-nc/4.0/" xml:lang="en">
 <license-p>This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)</license-p>
 </license>
 <ali:free_to_read />
 </permissions>
 <self-uri ns0:href="https://dongu-nec.ru/index.php/nec/article/view/533">https://dongu-nec.ru/index.php/nec/article/view/533</self-uri>
 <abstract xml:lang="en">
 <p>This study examines firm-level correlates of low-carbon innovation among Chinese agribusiness-related listed firms. Using a pooled firm-year panel of 316 firms and 3,920 observations from 1998 to 2021, it treats low-carbon patenting as a sparse innovation outcome rather than as a routinely observed continuous variable: 93.1% of firm-year observations record no low-carbon patent application. The empirical design separates two descriptive margins: whether a firm files at least one low-carbon patent in a given year and how the unconditional annual count varies across the full firm-year sample. The count outcome is estimated with pooled Poisson pseudo-maximum likelihood, while the occurrence margin is examined with pooled linear probability, logit, and complementary log-log specifications. The explanatory variables are organized into resource-capability conditions, adjacent innovation stocks, and supplementary linkage proxies. Across specifications, firm size and selected balance-sheet structure variables are the most stable correlates of low-carbon patenting. Pre-period green patent stock is the strongest adjacent innovation correlate. Because green and low-carbon patent categories may overlap, the study uses exact patent-level overlap exclusion as the main measurement check, and the positive green-stock association remains. Pre-period digital patent stock is more sensitive to model design and is more visible in occurrence and onset evidence than in full-sample count models. Branch-based geographic-reach proxies and static equity-link proxies add only limited incremental explanatory value after firm characteristics and lagged innovation stocks are controlled for. The findings are descriptive rather than causal and mainly reflect between-firm differences in the pooled panel. They suggest that low-carbon patenting in this sector is concentrated among larger and more innovation-capable firms rather than being a general outcome of network expansion.</p>
 </abstract>
 <trans-abstract xml:lang="ru">
 <p>В данном исследовании рассматриваются корреляции низкоуглеродных инноваций на уровне фирм среди китайских компаний агробизнеса, зарегистрированных на бирже. Используя объединенную панель данных за год из 316 фирм и 3920 наблюдений за период с 1998 по 2021 год, рассматривается патентование низкоуглеродных технологий как редкий инновационный результат, а не как регулярно наблюдаемая непрерывная переменная: в 93,1% наблюдений за год, проведенных фирмами, не зафиксировано ни одной заявки на низкоуглеродный патент. Эмпирический анализ позволяет разделить два показателя: подает ли фирма хотя бы один патент на низкоуглеродную продукцию в течение определенного года и как меняется безусловный годовой показатель по всей выборке за год работы фирмы. Результат подсчета оценивается с помощью объединенной пуассоновской псевдомаксимальной вероятности, в то время как предел вероятности возникновения оценивается с помощью объединенной линейной вероятности, логит-коэффициента и дополнительных логарифмических спецификаций. Независимые переменные сгруппированы по условиям ресурсных возможностей, смежным запасам инноваций и дополнительным показателям взаимосвязи. С точки зрения технических требований, размер фирмы и выбранные переменные структуры баланса являются наиболее стабильными показателями, влияющими на патентование низкоуглеродных технологий. Запас патентов на «зеленые» технологии, накопленный до истечения срока действия, является наиболее сильным показателем, связанным с инновациями. Поскольку категории патентов «зеленый» и «низкоуглеродистый» могут пересекаться, в исследовании в качестве основного критерия измерения используется точное исключение совпадений на уровне патентов, и сохраняется положительная связь между «зеленым» и «низкоуглеродистым» запасами. Запас цифровых патентов, выданных до начала периода, более чувствителен к дизайну модели и более заметен в свидетельствах о появлении и сроках действия, чем в моделях с полным подсчетом выборок. Прокси с географическим охватом на основе филиалов и статические прокси с привязкой к акционерному капиталу дают лишь ограниченную дополнительную объяснительную ценность после проверки характеристик фирмы и запаздывающих инновационных запасов. Полученные результаты носят скорее описательный, а не причинно-следственный характер, и в основном отражают различия между фирмами в объединенной группе. Они предполагают, что патентование низкоуглеродных технологий в этом секторе сосредоточено среди более крупных и способных к инновациям фирм, а не является общим результатом расширения сети.</p>
 </trans-abstract>
 <kwd-group xml:lang="en">
 <kwd>agribusiness-related listed firms</kwd>
 <kwd>low-carbon innovation</kwd>
 <kwd>green patents</kwd>
 <kwd>digital patents</kwd>
 <kwd>sparse outcomes</kwd>
 <kwd>PPML</kwd>
 <kwd>China</kwd>
 </kwd-group>
 <kwd-group xml:lang="ru">
 <kwd>листинговые компании агробизнеса</kwd>
 <kwd>низкоуглеродные инновации</kwd>
 <kwd>«зеленые» патенты</kwd>
 <kwd>цифровые патенты</kwd>
 <kwd>редкие результаты</kwd>
 <kwd>PPML</kwd>
 <kwd>Китай</kwd>
 </kwd-group>
 </article-meta>
 </front>
 <body>
 <p>[Полный текст статьи отсутствует в исходных данных. Необходимо добавить текст из PDF или другого источника.]</p>
 </body>
 <back>
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