Does travel closer to TOD have lower CO2 emissions? Evidence from ride-hailing in Chengdu, China

Understanding the effect of proactive personality and perceived consumer effectiveness on low-carbon travel intention

Choosing low-carbon transportation is an effective strategy for mitigating carbon emissions. This study utilized the Theory of Planned Behavior (TPB) framework to investigate the influences of proactive personality and perceived consumer effectiveness on low-carbon travel intention. By surveying urban residents, we examined the effects of subjective norm, behavioral attitude, perceived behavioral control, proactive personality, and perceived consumer effectiveness on low-carbon travel intention. The findings indicated that subjective norm, behavioral attitude, perceived behavioral control, proactive personality, and perceived consumer effectiveness have positive impacts on low-carbon travel intention. Notably, subjective norms have the greatest influence on behavioral intention, followed by proactive personality, while perceived consumer effectiveness has the least impact. Furthermore, this study identified attitude and perceived behavioral control as mediating factors between proactive personality, perceived consumer effectiveness, and low-carbon travel intentions. These findings reaffirmed the universal applicability of the TPB in individual decision-making contexts. The results also suggested that to promote low-carbon travel, it is crucial to not only focus on the social aspects of urban residents but also leverage the potential of individuals with a proactive personality.

Uncovering the Carbon Emission Intensity and Reduction Potentials of the Metro Operation Phase: A Case Study in Shenzhen Megacity

The huge energy consumption of metro operations has become a significant challenge faced by the urban public transportation sector to achieve low-carbon development. Using Shenzhen as an example, this study has made efforts to quantify the metro's energy consumption and carbon emission intensity during the operation phase by using the Life Cycle Assessment approach. Furthermore, this study evaluates the actions that can be taken to reduce energy consumption and emissions. A comparative analysis between metros and other public transportation modes has also been conducted. The results show that the annual carbon emissions from the metro's operation phase in Shenzhen city increased from 63,000 t CO₂e in 2005 to 1.3 Mt CO₂e in 2021, and the historically accumulated carbon emissions are 9.5 Mt CO₂e. The unit operating mileage, the unit station area, and the per capita carbon emission intensity were 2.1 kg CO₂e/km, 132.5 kg CO₂e/m², and 0.6 kg CO₂e per capita (13th Five-Year Plan Period), respectively. By continually promoting the low-carbon operation of the subway, the cumulative carbon savings could reach 0.1 Mt CO₂e (2022-2035).

Urban Intelligence for Carbon Neutral Cities: Creating Synergy among Data, Analytics, and Climate Actions

Cities are critical research subjects in carbon neutrality, considering they produce more than 70% of greenhouse gas emissions and their crucial role in taking climate actions. The pathway towards a greener society requires consensus, intelligence, and actions among global cities as a network of carbon neutral cities (CNC). Considering cities as complex system-of-systems, synergy among various sub-systems can create co-benefits through the progress towards carbon neutrality. Large volume, velocity, and variety of urban data provide new opportunities for quantifying, analyzing, and visualizing environmental–social–technical dynamics in urban systems. Rich data resources, advanced analytics, and climate actions collectively enable urban intelligence by leveraging data from heterogeneous sources with different spatial granularity and temporal frequency. Such intelligence can promote synergy across sub-systems and domains to support more responsive, precise, proactive planning, policy, and managerial actions. With a discussion on future innovation in urban intelligence for CNC, this paper presents conclusions on how urban intelligence can promote a smarter and greener society.