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        <datestamp>2026-10-01T17:52:57Z</datestamp>
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          <dc:title>&lt;p&gt;Instrumental variable strategy results.&lt;/p&gt;</dc:title>
          <dc:creator>Wenhe Lin (12770325)</dc:creator>
          <dc:creator>Yumin Guo (527677)</dc:creator>
          <dc:creator>Bingxuan Wang (14847715)</dc:creator>
          <dc:creator>Qiuwang Cheng (25158107)</dc:creator>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Developmental Biology</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>strengthening technological innovation</dc:subject>
          <dc:subject>organizational resource base</dc:subject>
          <dc:subject>china &amp;# 8217</dc:subject>
          <dc:subject>development remains limited</dc:subject>
          <dc:subject>artificial intelligence development</dc:subject>
          <dc:subject>alleviate development bottlenecks</dc:subject>
          <dc:subject>reliable electricity supply</dc:subject>
          <dc:subject>mechanism analyses show</dc:subject>
          <dc:subject>level artificial intelligence</dc:subject>
          <dc:subject>energy infrastructure construction</dc:subject>
          <dc:subject>enterprises &amp;# 8217</dc:subject>
          <dc:subject>chinese listed enterprises</dc:subject>
          <dc:subject>xlink "&gt; ultra</dc:subject>
          <dc:subject>level ai development</dc:subject>
          <dc:subject>energy supply</dc:subject>
          <dc:subject>ai development</dc:subject>
          <dc:subject>level cities</dc:subject>
          <dc:subject>analyses indicate</dc:subject>
          <dc:subject>tech enterprises</dc:subject>
          <dc:subject>owned enterprises</dc:subject>
          <dc:subject>enterprises located</dc:subject>
          <dc:subject>well recognized</dc:subject>
          <dc:subject>transmission projects</dc:subject>
          <dc:subject>technology adoption</dc:subject>
          <dc:subject>study highlights</dc:subject>
          <dc:subject>study finds</dc:subject>
          <dc:subject>study addresses</dc:subject>
          <dc:subject>staggered rollout</dc:subject>
          <dc:subject>side reforms</dc:subject>
          <dc:subject>power system</dc:subject>
          <dc:subject>period difference</dc:subject>
          <dc:subject>natural experiment</dc:subject>
          <dc:subject>level importance</dc:subject>
          <dc:subject>key component</dc:subject>
          <dc:subject>inland regions</dc:subject>
          <dc:subject>chair duality</dc:subject>
          <dc:description>&lt;div&gt;&lt;p&gt;Ultra-high voltage (UHV) transmission projects are a key component of China’s energy supply-side reforms, improving the efficiency and reliability of the power system. While their macro-level importance is well recognized, evidence on their impact on firm-level artificial intelligence (AI) development remains limited. This study addresses this gap by exploiting the staggered rollout of UHV transmission projects across prefecture-level cities as a quasi-natural experiment. Using a multi-period difference-in-differences (DID) approach on a panel of Chinese listed enterprises from 2007 to 2023, this study finds that UHV transmission projects significantly promote enterprises’ AI development. The effect is stronger for non-state-owned enterprises, enterprises located in inland regions, enterprises with CEO-chair duality, and high-tech enterprises. Mechanism analyses show that UHV transmission projects enhance enterprises’ AI development by strengthening technological innovation, expanding enterprises’ organizational resource base, and improving regional financial development. Further analyses indicate that UHV transmission projects also improve resource allocation efficiency and alleviate development bottlenecks. By linking physical energy infrastructure with firm-level AI development, this study highlights the importance of reliable electricity supply for technology adoption and digital transformation.&lt;/p&gt;&lt;/div&gt;</dc:description>
          <dc:date>2026-10-01T17:52:50Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pone.0355269.t003</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/_p_Instrumental_variable_strategy_results_p_/34049798</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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