This study examines how patent portfolios relate to market capitalization in an economy where intangible assets account for roughly 90% of corporate value. Using a 10-year panel (2015-2024) of 40 public firms across technology, pharmaceutical, and consumer sectors, I estimate OLS, firm fixed-effects, and generalized estimating equation models within the Hall-Jaffe-Trajtenberg valuation framework to test whether citation-weighted patent quality outperforms raw patent quantity as a predictor of market value. Results show that citation elasticity (β₃ = 0.26-0.31, p < 0.01) is approximately 2-3× larger than the elasticity of patent productivity (β₂ = 0.06-0.12), indicating that patent quality is substantially more informative for valuation than simple counts. Technical founder-CEOs sustain about 22% higher R&D intensity and exhibit a positive interaction with citation quality, while geographic filing breadth serves as a cost-effective quality proxy: only 3.3% of Chinese patents are filed internationally versus more than 70% for advanced economies. Sector heterogeneity is pronounced, with the patent-value relationship strongest in pharmaceuticals (ρ ≈ 0.71), moderate in semiconductors, and weak and often statistically insignificant in consumer goods. Case studies of NVIDIA's decade-long CUDA investment, Intel's forgone AI platform bets, and a projected 2025-2030 pharmaceutical "patent cliff" illustrate the mechanisms by which technical leadership and AI-enabled drug discovery reshape the economic returns to patents. The findings imply that conventional patent metrics systematically mismeasure innovation value and suggest that investors, corporate strategists, and policy makers should overweight citation velocity and geographic breadth-rather than raw counts-when evaluating innovation-driven market value.

Using data from Chinese non-financial listed firms over the 2009-2021 period, this study examines the link between cybersecurity and total factor productivity. The results suggest that cybersecurity can improve total factor productivity, a finding robust to multiples tests. Further investigation confirms that this relationship arises from information multiplication and capital deepening. It is particularly pronounced after the implementation of the Cybersecurity Law or among firms with strong digital innovation activities. In contrast, for firms whose senior executives have an information technology background, the moderating effect is not significant. However, the relationship is more pronounced for firms with larger scale, that are more technology-intensive, or operating in industries with above-average competition. Policy wise, these findings provide new evidence on the economic value of cybersecurity and offer policy implications for improving cybersecurity governance and aligning security investment with information and communications technology development to enhance resource allocation efficiency and sustainable digital growth.

This paper examines how potential disruptions from generative artificial intelligence (AI) to firms' core businesses affect their cross-industry acquisition decisions. Using data on Chinese A-share listed firms, we employ a large language model to assess the potential positive and negative effects of generative AI on core businesses and construct a measure of net AI risk exposure. We measure cross-industry M&amp;amp;A using the semantic distance between acquirers' and targets' business descriptions. A difference-in-differences analysis exploiting the commercial diffusion of generative AI shows that more exposed firms acquire more distant targets, with an effect equivalent to 8.2% of the sample mean and robust to alternative specifications. The effect is stronger among firms with narrower innovation boundaries and more concentrated business portfolios, consistent with innovation capability reconfiguration and strategic risk diversification. It is also stronger among firms with smaller market shares, in more competitive industries, and in more marketized regions. Further analyses show that high-exposure firms acquire targets with higher AI patent shares and receive more favorable announcement-day market reactions to distant acquisitions. Our findings suggest that potential AI-related disruptions shape acquisition target selection and firms' business boundaries.

We study how expanding access to intellectual property (IP)-backed credit affects firm innovation and aggregate total factor productivity (TFP). Exploiting China's IP-backed financing pilot as a quasi-natural experiment, we use difference-indifferences estimates to show that the policy increases R&D participation, raises firm-level productivity, and reduces capital misallocation. To interpret these effects and quantify their aggregate implications, we develop a dynamic model of heterogeneous firms that borrow subject to collateral constraints backed by tangible assets and IP, and choose R&D investment and the accumulation of wealth. R&D raises future productivity and creates collateralizable IP. We estimate the model by the method of simulated moments, using the policy-induced shift in R&D participation to identify the change in IP pledgeability. The estimated model implies that a nationwide adoption of IP-backed financing would raise long-run TFP by 14%, with roughly two-thirds coming from static gains due to improved capital allocation and the remainder from dynamic gains driven by higher innovation that accumulate over time. Relative to a comparable expansion of lending backed by tangible collateral, IP-backed financing delivers larger productivity gains by easing constraints for high-productivity but low-wealth firms and by strengthening incentives to accumulate IP as collateral for future borrowing.

Our study examines dynamic innovation through the U.S.-Sino industrial-chain networks and its asset-pricing and financial-fundamental implications amid U.S.-China network disruptions. Innovation synergy captures similarity between firms’ learned innovation processes, in which firm characteristics and network information are used to predict patent output. Using an annually reconstructed joint network of Chinese and U.S. firms, we form peer-return, peer-R&amp;amp;D, and peer-patent measures and estimate their associations separately in the two markets. The annual R&amp;amp;D- and patent-based measures are positively associated with firms' own R&amp;amp;D intensity in both markets, their gross-profitability associations are stronger for large firms, and tangible-asset associations become more positive after 2017. The R&amp;amp;D- and patent-based long–short portfolios have positive Fama–French three-factor alphas in both markets, while the return-based signal is stronger in China than in the United States.

Enhancing the performance of corporate green supply chain management (GSCM) represents a critical enabler for delivering on the central imperatives of the global low-carbon transition. Using panel data of Chinese listed companies from 2003 to 2022, this study employs a staggered difference-in-differences (DID) approach to examine the impact of the Low-Carbon City Pilot (LCCP) policy on firms' GSCM performance. The results show that the LCCP policy significantly improves firms' GSCM performance, and this effect remains robust across a range of sensitivity tests. Mechanism analyses reveal that the policy operates primarily by stimulating green innovation and increasing investment in environmental protection equipment. Furthermore, financial constraints play a significant moderating role: the positive effect of the policy on GSCM performance is stronger when firms face fewer financing constraints. Heterogeneity analysis indicates that the policy effects are more pronounced among non-heavy-polluting enterprises, those located in resource-dependent cities, and ISO 14001-certified enterprises. This study provides novel micro-level evidence on the environmental governance effects of place-based climate policies, and offers key policy implications for scaling up low-carbon city pilots and promoting corporate supply chain decarbonization.

Digital technologies are often portrayed as disruptive levers for decarbonisation, yet many digital green initiatives improve transparency and efficiency without redesigning how environmental value is authorised, coordinated, and appropriated. We address this puzzle by theorising green digital business model innovation as the evolution of venture-level configurations under multi-domain selection in policy-steered sustainability transitions. Building on the activity-system view of business models and on innovation-studies, we define business model radicality as coupled reconfiguration across content, structure, and governance rather than technology novelty alone. We further theorise a cross-domain convergence requirement: configurations are retained only when they align simultaneously with institutional role allocations, market interfaces, technological infrastructures, and ecological verification demands. A comparative longitudinal study of three blockchain-enabled initiatives in China’s energy-finance nexus shows that radical disintermediation is screened out early when any single domain withholds authorisation or credible settlement, whereas incumbent-compatible configurations are retained through sequencing, interpretive pruning, and boundary-spanning alignment work. The study contributes to innovation studies by treating business models as meso-level selection objects, sharpening configuration-level radicality, and explaining why digital green opportunities often remain incremental in regulated transition systems.

This paper exploits China’s culture-oriented institutional reform as a quasinatural experiment to examine the impact of cultural policies on water pollution. Using a difference-in-differences approach with city-level panel data from 2003 to 2020, we find that the reform leads to a significant reduction in water pollution in pilot cities. Heterogeneity analysis indicates that the effect is more pronounced in cities with better economic performance, higher administrative status, richer tourism resources, and more abundant human capital. The reform prompts a shift in local industrial structure toward less-polluting sectors, with a marked contraction in pollution-intensive activities. It also strengthens local environmental regulation and enhances green innovation, thereby contributing to pollution reduction. Our findings highlight the spillover environmental effects of policies not explicitly designed for environmental purposes.

This study examines how emerging market multinational enterprises (EMNEs) respond to cross-national diversity in environmental regulation. Prior research suggests that internationalization exposes EMNEs to diverse environmental standards and may facilitate regulatory learning, yet offers a limited understanding of the mechanisms through which such exposure translates into green innovation. Drawing on institutional theory, we argue that green innovation is particularly fostered when EMNEs operate across host countries whose environmental regulatory regimes are more stringent than those in the home country, and when such exposure varies across multiple host locations. We theorize that this form of environmental regulatory diversity creates learning pressures and legitimacy incentives that motivate EMNEs to upgrade their environmental capabilities through green innovation. Using panel data comprising 9,362 firm-year observations from 1,088 Chinese MNEs over the period 2008–2021, we find support for our arguments. We further show that these effects are contingent on organizational and institutional conditions, specifically top management teams’ technological expertise and the strength of intellectual property enforcement in home-country locations. These findings advance research on EMNE learning and sustainable innovation under complex global environmental governance.

Despite strong GDP growth and a favourable demographic profile, India faces an impending jobs crisis. A large share of the workforce remains employed in lowproductivity agriculture, while many new labour market entrants are absorbed into the persistently large informal sector. By contrast, China's rapid ascent was driven by manufacturing-led, export-oriented industrialisation, underpinned by large inflows of foreign direct investment and sustained technology transfer. India's manufacturing base remains modest in contrast. The bulk of well-paid, formal employment continues to be concentrated in the high-skill services sector. This paper contrasts the development trajectories of these two economies and identifies several underutilised jobs-growth levers in India: manufacturing, goods exports, manufacturing-oriented foreign direct investment and innovation. All of these remain underdeveloped, yet together they offer a pathway to more labour-absorbing, durable growth. Leveraging them effectively would be central to achieving India's 'Viksit Bharat 2047' ambition of attaining high-income status. The scale of India's challenge to employ eight to ten million labour-market entrants per year implies that job creation must become an explicit policy priority. This calls for greater trade openness, particularly with Asia and Europe, to integrate India into Asia-centric global supply chains as an alternative to China. Labour market reform is equally critical, making the effective implementation of the new labour codes essential. Strengthening innovation ecosystems and realigning education and skills policies to support industrialisation are also key. Without these structural shifts, India's current pattern of jobless growth risks transforming its demographic dividend into a long-term liability.