Optimizing public investment in urban green infrastructure under water scarcity is a core challenge in resource economics. Against the backdrop of global climate change—characterized by rising temperatures, increased frequency and intensity of droughts, and altered precipitation patterns—this study addresses the critical knowledge gap in quantifying the economic returns on the physiological adaptations of urban trees, which are central to their value as natural capital. We integrated dual-water isotope (δ²H, δ¹⁸O) and leaf carbon isotope (δ¹³C) analyses to mechanistically decode the water use strategy of Machilus yunnanensis (M. yunnanensis) in drought-prone Kunming, China. The results show strategic seasonal plasticity: a shift from shallow soil water (10–50 cm) in the wet season to deeper soil sources (50–90 cm) and stem reserves in the dry season, coupled with a dynamic, diurnally variable water use efficiency (WUE_13C). We then constructed a transparent economic valuation model translating these traits into three quantifiable benefit streams: (1) operational cost savings (EV₁) from reduced irrigation demand; (2) enhanced marginal productivity of water (EV₂) in ecosystem service generation; and (3) climate resilience value (EV₃) via mitigated mortality risk. Our “Water–Carbon–Economy” nexus framework provides a generalizable methodology for assessing the cost-effectiveness and risk-adjusted returns of urban forest species, demonstrating that tree selection based on such eco-efficient traits is not merely an ecological choice but a sound economic investment, offering direct implications for budget-constrained municipalities seeking to maximize green infrastructure benefits under climate uncertainty.

Optimizing public investment in urban green infrastructure under water scarcity is a core challenge in resource economics. This study addresses the critical knowledge gap in quantifying the economic returns on physiological adaptations of urban trees, which are central to their value as natural capital. We integrate dual water isotopes (δ²H, δ¹⁸O) and leaf carbon isotope (δ¹³C) analysis to mechanistically decode the water-use strategy of Machilus yunnanensis in drought-prone Kunming, China. Results show strategic seasonal plasticity: a shift from shallow soil water (10–50 cm) in the wet season to deeper soil sources (50–90 cm) and stem reserves in the dry season, coupled with a dynamic, diurnally variable water-use efficiency (WUE). We then construct a transparent economic valuation model translating these traits into three quantifiable benefit streams: (1) Operational cost savings (EV₁) from reduced irrigation demand; (2) Enhanced marginal productivity of water (EV₂) in ecosystem service generation; and (3) Climate resilience value (EV₃) via mitigated mortality risk. Our “Water-Carbon-Economy” nexus framework provides a generalizable methodology for assessing the cost-effectiveness and risk-adjusted returns of urban forest species. It demonstrates that selecting trees based on such eco-efficient traits is not merely an ecological choice but a sound economic investment, offering direct implications for budget-constrained municipalities seeking to maximize the benefits of green infrastructure under climate uncertainty.

Against the backdrop of China’s “Carbon Peaking and Carbon Neutrality” goal, the steel industry faces mounting pressure to improve its environmental accounting information disclosure as part of its green and low-carbon transition. This study evaluates the quality of environmental accounting disclosure among 30 listed Chinese steel companies from 2019 to 2023, using the analytic hierarchy process (AHP) and content analysis across disclosure channels and content dimensions. Three key findings are revealed: (1) Annual reports remain the dominant disclosure channel, while the share of standalone environmental reports rises from 3.33% to 53.33% but remains low and inconsistent. (2) Disclosure exhibits a pattern of “strong hard indicators but weak soft indicators and excessive positive information but scarce negative information”; environmental liability carries the highest weight (0.3083); and the quantitative disclosure of industrial solid waste and other specific metrics remains below 30%. (3) A weight-driven stratification pattern exists: high-weight hard indicators have improved under policy and market pressure, whereas low-weight soft indicators and negative information remain largely neglected. Based on these findings, this study proposes optimization paths from institutional norms, corporate governance, and external supervision. The conclusions provide practical implications for enhancing disclosure quality and supporting the green transformation of the steel industry under the “Carbon Peaking and Carbon Neutrality” strategy.