Fangcheng Tang

Prof. Dr. Fangcheng Tang


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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SSShuo Sun
Shuo Sun
BGBing Gu
Bing Gu
FTFangcheng Tang
Fangcheng Tang
Cross-category innovation in digital platform ecosystems is increasingly pivotal for competitive reconfiguration, and the value it generates for users primarily stems from the benefits of network effects. By extending the spatial competition framework of the Hotelling model through a four-stage sequential game comprising category competition, we formalize the strategic mechanism for expanding network effects governing benchmark competition and category dynamics. The cross-category innovation strategy proposed in this paper offers valuable insights in three key areas: investment in core technological advantages, reconstruction of user cognitive boundaries, and strengthening ecological dependency within the ecosystem. By transcending the limitations in the explanatory power of traditional management theories for cross-organizational boundary issues, this study integrates digital contexts into its analytical framework, thus providing a novel perspective for understanding the dynamic processes of cross-category innovation in digital platform ecosystems.

13 November 2025 in Energies
Verified
Journal Article
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SLShasha Liu
Shasha Liu
FTFangcheng Tang
Fangcheng Tang
Against the global rollout of Carbon Border Adjustment Mechanisms (CBAMs), carbon tariffs have emerged as a core tool for developed economies to internalize environmental externalities—especially for energy-intensive imports that dominate cross-border carbon flows. However, emission information asymmetry, a critical barrier to implementing cross-border energy and environmental policies, undermines the design of optimal carbon tariffs, as it distorts the link between tariff levels and actual fossil energy-related emissions. This study develops a two-country analytical model to examine how biased assessments of exporters’ carbon intensity influence optimal tariff settings, exporters’ strategic behavior, and aggregate carbon emissions—with a focus on energy-intensive production contexts. The results show that underestimating carbon intensity reduces exporters’ compliance costs, incentivizing emission concealment; this weakens tariffs’ environmental stringency and may raise global emissions. Overestimation, by contrast, inflates exporters’ marginal costs, discouraging green investment and causing emission displacement rather than reduction. The analysis highlights a policy feedback loop wherein misjudged emission information distorts both trade competitiveness and environmental performance. This study concludes that a transparent, accurate, and internationally verifiable carbon accounting system is essential: it not only facilitates the effective implementation of CBAM but also aligns optimal carbon tariffs with CBAM’s dual goals of climate action and trade equity, while supporting global energy transition efforts.