Shuyi Hu

This study examines how the specificity of corporate climate commitments relates to environmental resource allocation and whether board independence conditions these associations. We link sustainability reports with financial, governance, and environmental data for 10,767 firm-year observations from 1864 Chinese listed firms, covering report years 2010–2022 and environmental outcomes through 2023. Commitments are classified as No Commitment, General Commitment (GC), or Quantified Commitment (QC). Firm- and year-fixed-effects models examine positive environmental expense or capitalized environmental asset records, monetary amounts, and intensities across alternative observation horizons. In the main full-sample analysis, GC is associated with a higher probability of positive environmental records, whereas QC shows no stronger association than GC. Greater board independence is associated with a more favorable QC–GC contrast. These patterns vary with data coverage and do not consistently extend to greater amounts or intensity, with some asset-based estimates showing negative associations. A sensitivity analysis using an alternative coding procedure yields statistically insignificant estimates for the GC association and the comparative moderation contrast. The study links research on climate commitments to environmental accounting by distinguishing the presence of recorded resources from their monetary scale.

Green mergers and acquisitions (M&A) may enable firms to acquire external environmental technologies, assets, and organizational capabilities. However, whether green M&A is associated with subsequent green innovation, how this association evolves across innovation types and time horizons, and whether prior carbon information disclosure conditions this process remain unclear. Using 40,923 firm year observations of Chinese A-share-listed firms from 2012 to 2024, this study combines licensed green M&A data from Zhixing Data Analytics, green patent data from CNRDS, and carbon disclosure, financial, and corporate governance data from CSMAR. The baseline treatment identifies firm years in which at least one green M&A transaction first announced during the year was subsequently recorded as completed. The analysis employs firm and year fixed-effects models, common-sample distributed-lag specifications, formal cross-type coefficient comparisons, forward-outcome tests, propensity score matching, entropy balancing, and alternative measures and specifications. Green M&A is positively associated with total green patenting. The baseline coefficient of 0.045 implies an approximately 4.65% increase in one plus the number of total green patent applications. The contemporaneous association is stronger for green utility model patenting than for green invention patenting, whereas the association with invention patenting becomes more evident in subsequent periods. Prior carbon information disclosure positively moderates the association between green M&A and one-year-ahead invention patenting (β = 0.220, p = 0.018), while the corresponding moderation estimates for total and utility model patenting are not statistically significant. The findings are supported by observable selection adjustments and several alternative measurements and specifications, although fixed-effects PPML estimates using the original patent counts are not statistically significant. This study provides an integrated framework for understanding how innovation timing and prior information governance shape the green M&A dilemma. The results suggest that regulators and investors should assess green acquisitions using credible pre-acquisition carbon disclosure and post-acquisition innovation trajectories rather than relying on environmental transaction labels alone.

Against the backdrop of China’s “dual carbon” goals, investigating whether market-based environmental regulations can effectively induce technological upgrading is critical for achieving a sustainable low-carbon transition. This study adopts a staggered difference-in-differences (DID) approach within a two-way fixed-effects framework, supplemented by propensity score matching (PSM-DID), to identify the causal impact of the carbon emission trading (CET) pilot policy. The research utilizes a comprehensive panel dataset of A-share listed companies in heavy-polluting industries from 2010 to 2024, incorporating IPC-matched green patent application data to provide a granular assessment of corporate innovation performance. The empirical findings reveal a structural divergence: while the CET policy promotes green innovation in state-owned enterprises (SOEs), it exhibits a potential “crowding-out” effect on private enterprises, a relationship further explained by the mechanisms of carbon information disclosure and financing constraints. These results suggest that the “Porter Effect” in emerging markets is highly conditional on institutional resource endowments, implying that policymakers must complement market incentives with differentiated financial support and enhanced transparency standards to foster a more equitable innovation ecosystem.