Daoping Wang

With the advancement of ubiquitous connectivity and autonomous intelligence, future urban transportation systems are expected to become increasingly integrated across modes and contexts, necessitating a scalable and generic city-wide urban traffic prediction framework for traffic management. However, most existing studies focus on traffic flow prediction for a specific transport mode under a single scenario or time granularity, limiting their ability to generalize across diverse transport modes and scenarios. To solve these challenges, we propose a large language model (LLM)-based scalable and generic city-wide urban traffic prediction framework (LLM-UTP) for short-term traffic flow prediction. It captures the generic trends and specific fluctuations across different transport modes and scenarios and consists of three parts: a trend data enhancement module, a spatiotemporal feature encoding module, and an LLM module. Extensive experiments conducted on 11 large-scale real-world traffic datasets from 29 cities/areas covering a wide range of transport modes, traffic scenarios, and time granularities to validate the proposed model's complexity, scaling law, scalability, generality, and predictive performance, demonstrating its superiority. These results highlight the practical potential of LLM-UTP as a scalable and generic foundation model for intelligent traffic management and decision-making in future smart cities.

This paper examines the global effects of sustainable finance policies on firms' greenhouse gas (GHG) emission reductions. Drawing on a framework that distinguishes between the motives for implementing different sustainable finance policies and the relevant instruments used to achieve them, we analyze data from over 10,000 public firms in 64 economies over 2002–2021 using a quasi–difference‐in–difference‐in‐differences strategy. We find that countries with greater numbers of sustainable finance policies are associated with firm‐level Scope 1 GHG emission reductions in carbon‐intensive sectors. These effects are stronger/more pronounced in countries employing promotional (rather than prudential), as well as incentive‐based (rather than informational) types of sustainable finance policies. Additional scenario analysis further confirms our critical insights: Simply maintaining the 2010–2020 rate of new policy creation is insufficient. To meet midterm climate goals, the policy focus must shift toward a set of promotional and incentive‐based policies designed specifically to regulate investors. Our results speak to both the cumulative and differentiated nature of different sustainable finance policies and their effects on firms' nonfinancial performance, an understudied relationship. We call on policymakers to target their climate policies at investors and to provide support directly to developing countries.

Evidence shows an ongoing increase in the frequency and severity of global heatwaves, raising concerns about the future impacts of climate change and the associated socio-economic costs. Here, we develop a disaster footprint analytical framework by integrating climate models, epidemiological and hybrid input-output, and computable general equilibrium global trade models to estimate the mid-century socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labor productivity loss, and indirect losses due to economic disruptions cascading through supply chains. We find that the global heatwave days would increase by 104% in 2060 compared to 2022 under SSP585, and the global average annual number of heat-induced deaths would increase to around 1.12 million (0.85 ~ 1.39 million). For economic impacts, we show that the global annual incremental loss increases exponentially from 0.03±0.01 (SSP245) ~ 0.05±0.03 (SSP585) percentage points during 2030 – 2040 to 0.05±0.01 ~ 0.15±0.04 percentage points during 2050 – 2060. By 2060, the expected global economic losses reach a total of 0.6% ~ 4.6% with losses attributed to health loss (37%~45%), labor productivity loss (18%~37%), and indirect loss (12%~43%) under different SSPs. Small and medium-sized developing countries in Southeast Asia and Africa suffer the most from heat risks as well as regional supply chain disruptions.

More frequent heat events globally prompt behavioral adaptations such as reducing outdoor activities to relieve potential distress. The emergence of innovative daily life services in cities offers new avenues for implementing such adaptive strategies. Here we investigate whether urban residents augment food delivery consumption as an immediate response to heat in China. Analyzing extensive individual-level food delivery data from a major platform across 100 Chinese cities from 2017 to 2023, we observe a significant surge in lunchtime orders, exceeding 10%, as temperatures escalate from 20°C to 35°C, and reaching 20% at 40°C. These increments, coupled with reduced heat exposure via food delivery, are more pronounced among female, high-income, and older individuals, signifying varying degrees of benefit among specific vulnerable groups. Projections under the RCP8.5 global warming scenario indicate a persistent rise in food delivery demand and the associated mitigation of heat exposure by mid-century. Our findings underscore significant policy implications for climate change adaptation and highlight the pivotal role of urban daily life services in alleviating heat-related challenges for urban sustainability.

Evidence shows a continuing increase in the frequency and severity of global heatwaves^1,2, raising concerns about the future impacts of climate change and the associated socioeconomic costs^3,4. Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.