Fujian Province, China was chosen as the study area for estimating the marginal agricultural abatement costs within the province based on data for 2010–2020. The study employed these estimations as a pivotal factor affecting the efficien...

As the only Chinese city inscribed on all three major UNESCO Intangible Cultural Heritage (ICH) lists, Quanzhou boasts 7 world-class and 36 national-level ICH projects. This rich heritage provides a robust foundation for advancing the city’s cultural soft power. However, field investigations reveal prominent challenges in integrating ICH preservation with the development of urban cultural soft power. These bottlenecks include stagnant living transmission, insufficient synergy with urban development, limited dissemination impact, and a severe imbalance between conservation and commercial exploitation. Grounded in systems engineering principles, this study employs a mixed-methods approach encompassing field surveys (yielding 17 in-depth interviews conducted in March 2026), official dataset analysis(2015–2024), ArcGIS kernel density estimation, and comparative horizontal benchmarking against Suzhou and Chengdu—two national-level historical and cultural cities. We propose a five-dimensional enhancement framework—encompassing structural design, element optimization, conflict resolution, multi-stakeholder collaboration, and quality upgrading—to leverage ICH for soft power augmentation. Furthermore, the study delineates phased implementation mechanisms, quantifiable evaluation metrics, and monitoring tools, offering a theoretical reference and practical paradigm for historical cities aiming to elevate their cultural soft power through heritage resources.

The shadow price of agricultural carbon emissions serves as a key indicator for quantifying the cost of carbon reduction in agriculture and for reflecting the associated opportunity costs. It has substantial theoretical and practical relevance for the green development and low-carbon transformation of agriculture under the dual-carbon goals. It holds significant theoretical and practical value for advancing green development and low-carbon transformation in agriculture under the dual-carbon goals. Drawing on marginal productivity theory, this study classifies agricultural carbon emissions as a production factor, builds a translog production function model, and employs ridge regression to address multicollinearity. Using panel data from nine prefecture-level cities in Fujian Province (2010–2023), we measure the shadow prices of agricultural carbon emissions across cities and conduct robustness tests via stochastic frontier analysis (SFA), exploring spatiotemporal evolutionary patterns and the drivers of regional disparities. The key findings are as follows: (1) Fujian’s agricultural carbon emission shadow prices show an overall sustained upward trend, with the economic opportunity cost of per-unit carbon reduction rising consistently across cities. (2) Significant regional disparities exist—the average shadow prices in eastern and northern Fujian remain among the highest in the province, while southern Fujian remains at a lower level, forming a “southeastern coastal–northwestern inland” dual spatial pattern. (3) The shadow price of agricultural carbon emissions varies significantly across regions, with Zhangzhou, Fuzhou, and Nanping having the highest emission reduction costs and Xiamen having the lowest. By quantifying the spatiotemporal characteristics of the costs of agricultural carbon emission reduction in Fujian, this study provides an economic basis for formulating differentiated carbon reduction policies while identifying limitations such as the restricted scope of carbon source accounting and clarifying directions for future research.