Expanding carbon neutrality strategies: Incorporating out-of-boundary emissions in city-level frameworks

Cities are increasingly vital in global carbon mitigation efforts, yet few have specifically tailored carbon neutrality pathways. Furthermore, out-of-boundary indirect greenhouse gas (GHG) emissions, aside from those related to electricity and heat imports, are often overlooked in existing pathways, despite their significance in comprehensive carbon mitigation strategies. Addressing this gap, here we introduce an integrated analysis framework focusing on both production and consumption-related GHG emissions. Applied to Wuyishan, a service-oriented city in Southern China, this framework provides a holistic view of a city's carbon neutrality pathway, from a full-scope GHG emission perspective. The findings reveal the equal importance of carbon reduction within and outside the city's boundaries, with out-of-boundary emissions accounting for 42% of Wuyishan's present total GHG emissions. This insight highlights the necessity of including these external factors in GHG accounting and mitigation strategy development. This framework serves as a practical tool for cities, particularly in developing countries, to craft effective carbon neutrality roadmaps that encompass the full spectrum of GHG emissions.

Natural and human environment interactively drive spread pattern of COVID-19: A city-level modeling study in China

A novel Coronavirus COVID-19 has caused high morbidity and mortality in China and worldwide. A few studies have explored the impact of climate change or human activity on the disease incidence in China or a city. The integrated study concerning environment impact on the emerging disease is rarely reported. Therefore, based on the two-stage modeling study, we investigate the effect of both natural and human environment on COVID-19 incidence at a city level. Besides, the interactive effect of different factors on COVID-19 incidence is analyzed using Geodetector; the impact of effective factors and interaction terms on COVID-19 is simulated with Geographically Weighted Regression (GWR) models. The results find that mean temperature (MeanT), destination proportion in population flow from Wuhan (WH), migration scale (MS), and WH*MeanT, are generally promoting for COVID-19 incidence before Wuhan's shutdown (T1); the WH and MeanT play a determinant role in the disease spread in T1. The effect of environment on COVID-19 incidence after Wuhan's shutdown (T2) includes more factors (including mean DEM, relative humidity, precipitation (Pre), travel intensity within a city (TC), and their interactive terms) than T1, and their effect shows distinct spatial heterogeneity. Interestingly, the dividing line of positive-negative effect of MeanT and Pre on COVID-19 incidence is 8.5°C and 1 mm, respectively. In T2, WH has weak impact, but the MS has the strongest effect. The COVID-19 incidence in T2 without quarantine is also modeled using the developed GWR model, and the modeled incidence shows an obvious increase for 75.6% cities compared with reported incidence in T2 especially for some mega cities. This evidences national quarantine and traffic control take determinant role in controlling the disease spread. The study indicates that both natural environment and human factors integratedly affect the spread pattern of COVID-19 in China.