THE FUTURE OF CO2 EMISSIONS IN SPRAWLED URBAN AREAS

Road traffic carbon dioxide (CO2) has a fundamental role in global warming. Accurately estimating and understanding the spatio-temporal patterns of urban road traffic CO2 emissions plays a fundamental role in developing targeted reduction strategies. However, few studies have estimated CO2 emissions at the urban road scale with fine spatio-temporal resolution. Therefore, this study adopted a bottom-up method to estimate urban road traffic CO2 emissions using Global Positioning System (GPS) trajectory data. Urban road traffic CO2 emissions from individual vehicles are estimated using a vehicle trajectory-driven CO2 emission model The aggregated results are mapped within Traffic Analysis Zones (TAZ) to create CO2 emission distribution maps with minute-level temporal and road-level spatial resolution. The Multiscale Geographically Weighted Regression (MGWR) model is employed to analyze the impact of various elements of the built environment on urban road transport CO2 emissions. Experimental results indicate that road traffic CO2 emissions in Hangzhou have spatio-temporal heterogeneity. Road traffic CO2 emission hotspots are concentrated along main roads such as Shixiang Road, the City Ring Expressway, and Shiqiao Road. Further analysis indicates that population density, main road density, availability of bus stops, and length of bike lanes exert a significant influence on urban road transport CO2 emissions in Hangzhou. These findings enhance our recognition of the combined effects of the various elements of the built environment on urban road transport CO2 emissions. This study introduces a method for estimating CO2 emissions at the street level using vehicle trajectory information. It provides high spatio-temporal resolution CO2 emission distribution maps to support carbon emissions reduction strategies in urban transportation.

The precise and exhaustive discernment of factors influencing CO2 emissions underpins the advancement toward sustainable, low-carbon development. Although numerous studies have probed the correlation between predetermined proxy variables and carbon emissions, methodological constraints have often led to an inability to effectively discern carbon emission determinants among numerous potential variables or unravel complex, non-linear relationships, and interaction effects. To redress these research gaps, this research utilized machine learning models to correlate urban CO2 emissions with socioeconomic indicators. The model outputs were then visualized and interpreted using explainable methods. The findings indicated that the model successfully identified a comprehensive array of dominant influences on urban CO2 emissions, principally associated with local fiscal policies, land use, energy consumption, industrial development, and urban transportation. The findings further revealed a complex non-linear association between these factors and urban CO2 emissions; however, the majority of these variables displayed a prevalent propensity to intensify carbon emissions in correspondence with an increase in sample value. Additionally, these factors exhibited a complex interactive influence on urban CO2 emissions, with distinct pairings producing a suppressive effect exclusively at specific combination of sample values. Consequently, this research posited that a robust correlation between urban socioeconomic development and CO2 emissions in China remains to be established. Given the varied impacts of these influencing factors across different cities, a differentiated approach to development should be adopted when charting low-carbon trajectories.

Assessing the transition to low-carbon urban transport: A global comparison

Accurate and detailed accounting of energy-induced carbon dioxide (CO2) emissions is crucial to the evaluation of pressures on natural resources and the environment, as well as to the assignment of responsibility for emission reductions. However, previous emission inventories were usually production- or consumption-based accounting, and few studies have comprehensively documented the linkages among socio-economic activities and external transaction in urban areas. Therefore, we address this gap in proposing an analytical framework and accounting system with three dimensions of boundaries to comprehensively assess urban energy use and related CO2 emissions. The analytical framework depicted the input, transformation, transfer and discharge process of the carbon-based (fossil) energy flows through the complex urban ecosystems, and defined the accounting scopes and boundaries on the strength of ‘carbon footprint’ and ‘urban metabolism’. The accounting system highlighted the assessment for the transfer and discharge of socio-economic subsystems with different spatial boundaries. Three kinds methods applied to Beijing City explicitly exhibited the accounting characteristics. Our research firstly suggests that urban carbon-based energy metabolism can be used to analyze the process and structure of urban energy consumption and CO2 emissions. Secondly, three kinds of accounting methods use different benchmarks to estimate urban energy use and CO2 emissions with their distinct strength and weakness. Thirdly, the empirical analysis in Beijing City demonstrate that the three kinds of methods are complementary and give different insights to discuss urban energy-induced CO2 emissions reduction. We deduce a conclusion that carbon reductions responsibility can be assigned in the light of production, consumption and shared responsibility based principles. Overall, from perspective of the industrial and energy restructuring and the residential lifestyle changes, our results shed new light on the analysis on the evolutionary mechanism and pattern of urban energy-induced CO2 emissions with the combination of three kinds of methods. And the spatial structure adjustment and technical progress provides further elements for consideration about the scenarios of change in urban energy use and CO2 emissions.

Given the increasingly severe global climate change, the reduction in urban greenhouse gas emissions has become the common goal of all nations. As a widely concerned sustainable development strategy, green infrastructure investment (GII) aims to reduce urban carbon emissions, improve the efficiency of resource utilization, and improve environmental quality. However, the construction cycle of green infrastructure is long, and the construction process itself may produce carbon emissions; so, the final effect of GII on urban carbon emissions is unclear, which deserves our in-depth study. Further, is this effect having a time-lag effect? Is there only a simple linear relationship between GII and urban carbon emissions? Based on panel data from 235 Chinese cities from 2006 to 2019, this study conducted an econometric regression analysis using time-lag-effect and threshold-effect models. The results showed the following: (1) GII had a negative inhibitory effect on urban CO2 emissions. Adding one unit to the GII could reduce urban CO2 emissions by 0.032 units. (2) GII exhibited a time-lag effect on urban CO2 emissions, and the greatest reduction in CO2 emissions occurred in the third lag period. (3) GII had a threshold effect on urban CO2 emissions based on technological progress (TP). This paper used the static and dynamic panel threshold models to research separately, and obtained the corresponding regression results. In the static panel, the double threshold values for TP were 3.9120 and 6.8035. At different TP levels, GII had an inhibitory effect on CO2 emissions, but the coefficients were different. However, in the dynamic panel, the threshold value was 3.666. The threshold changed over time and the effect of GII on CO2 emissions shifted from facilitation to inhibition.

Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets

Urban traffic is an important source of global CO2 emissions. Uncovering the temporal and structural characteristics can provide scientific support to identify the variation regulation and main subjects of urban traffic CO2 emissions. The road class is one of the most important factors influencing the urban traffic CO2 emissions. Based on the annual traffic field monitoring work in 2014 and the localized MOVES model, this study unravels the temporal variation and structural characteristics of the urban traffic CO2 emissions and conducts a comparative analysis of expressway (5R) and arterial road (DB), two typical classes of urban roads in Beijing. Obvious differences exist in the temporal variation characteristics of the traffic CO2 emissions between the expressway and arterial road at the annual, week and daily scales. The annual traffic CO2 emissions at the expressway (5R, with 47271.15 t) are more than ten times than those of the arterial road (DB, with 4139.19 t). Stronger weekly “rest effect” is observed at the expressway than the arterial road. The daily peak time and duration of the traffic CO2 emissions between the two classes of urban roads show significant differences particular in the evening peak. The differences of the structural characteristics between the two classes of urban roads are mainly reflected on the contribution of the public and freight transportation. Passenger vehicles play a predominant role at both the two classes of urban roads. The public transportation contributed more at DB (24.76%) than 5R (5.47%), and the freight transportation contributed more at 5R (23.41%) than DB (3.49%). The results suggest that the influence of traffic CO2 emissions on the CO2 flux is significant at the residential and commercial mixed underlying urban areas with arterial roads (DB) but not significant at the underlying urban park area with expressway (5R) in this study. The vegetation cover in urban areas have effects on the CO2 reduction. Increasing the design and construction of the green space along the urban roads with busy traffic flow will be an effective way to mitigate the urban traffic CO2 emissions and build the low-carbon cities.

Understanding the relationship between urban growth and CO2 emissions is essential for sustainable urban and environmental planning in China. Even though some studies have been conducted in this regard, there is a lack of comprehensive studies that integrate socioeconomic and nighttime light (NL) data on both spatial and temporal scales. Therefore, using NL data as a proxy for urban growth, this study offers a novel approach to assess city size distribution (CD) and CO2 emission dynamics from 2000 to 2020 at the provincial and prefecture levels. The present study was conducted in three phases: (1) assessing the association between urban growth and socioeconomic characteristics; (2) measuring CD dynamics using corrected NL data; and (3) modeling CO2 emission dynamics through panel data analysis. While the Ordinary Least Squares (OLS) method examined the relationship between socioeconomic characteristics and urban growth, the CD dynamics were measured using Catteow’s formula. A panel unit root test, panel co-integration test, and panel regression analyses were performed to explore the relationship between urban growth and CO2 emissions. Results revealed that maximum NL data have stronger correlations with population, GDP, and EPC at the provincial level than at the prefecture level, with an average R2 range from 0.6219 to 0.8985. The analysis of CD dynamics revealed an increase in urban disparity, particularly among larger cities, with the q value rising from 0.7920 to 0.8268. CO₂ emissions expanded by 250.76% from 2000 to 2020, with the highest growth seen in coastal megacities. Panel unit root and co-integration tests confirmed a long-term relationship between urban growth and CO2 emissions at both scales. Panel regression analysis showed a positive and significant impact of urban growth on CO2 emissions at the national level and across all regions and provinces. These findings highlight the importance of sustainable urban planning strategies that incorporate socioeconomic characteristics with spatial and temporal considerations to reduce CO2 emissions in China. However, further research is necessary to explore multidimensional strategies for balancing urban expansion and CO2 emissions.

Factor decomposition analysis and causal mechanism investigation on urban transport CO2 emissions: Comparative study on Shanghai and Tokyo

Can China achieve its 2030 and 2060 CO2 commitments? Scenario analysis based on the integration of LEAP model with LMDI decomposition

Exploring the realization pathway of carbon peak and carbon neutrality in the provinces around the Yangtze river of China

Achieving Paris Agreement temperature goals requires carbon neutrality by middle century with far-reaching transitions in the whole society

In order to actively fulfill its international treaty obligations, China has established the goal of peaking CO2 emissions by 2030 and achieving carbon neutrality by 2060. Since 2018, when ecological civilization was written into the Constitution, the realization of carbon peak and neutrality goals has had an ideological foundation and a constitutional basis. China has formulated various special laws and built a 1 + N policy system to reduce carbon emissions, which together with the environmental protection law, climate change law, energy law and other related laws and regulations constitute a unified legal system and provide legal support to achieve carbon peak and neutrality goals. At the same time, China has taken advantage of the new national system with concentrated efforts and resources to delineate the different roles of the government and market mechanisms in carbon emission reduction, and to make the operation of the legal system of carbon peak and neutrality suitable for its actual situation by giving full paly to the initiative of both central and local governments. This article analyzes the current legal system and its characteristics in China in the process of achieving carbon peak and neutrality goals in the context of the new era, and outlooks on the improvement path of the legal system from both domestic and international dimensions. The practice, experience and development direction of China in the construction of the legal guarantee for carbon peak and neutrality goals can provide reference for other countries to achieve carbon reduction.

China and India together have more than one third of the world population and are two emerging economic giants of the developing world now experiencing rapid economic growth, urbanization, and motorization. The urban transportation sector is a major source of carbon dioxide (CO2) emissions in China and India. The goal of this study is to analyze the characteristics and factors of CO2 emissions produced by commuters in Chinese and Indian cities and thus to identify strategies for reducing transportation CO2 emissions and mitigating global climate change. Xi'an in China and Bangalore in India were chosen as two case study cities for their representativeness of major cities in China and India. The trends of CO2 emissions produced by major traffic modes (electric motors, buses, and cars) in major cities of China and India were predicted and analyzed. The spatial distributions of CO2 emissions produced by commuters in both cities were assessed using spatial analysis module in ArcGIS (Geographic Information System) software. Tobit models were then developed to investigate the impact factors of the emissions. The study has several findings. Firstly, in both cities, the increase of vehicle occupancy could reduce commuting CO2 emissions by 20 to 50 % or conversely, if vehicle occupancy reduces, an increase by 33.33 to 66.67 %. It is estimated that, with the current increasing speed of CO2 emissions in Xi'an, the total CO2 emissions from electric motors, buses, and cars in major cities of China and India will be increased from 135 x 10(6) t in 2012 to 961x 10(6) t in 2030, accounting for 0.37 to 2.67 % of the total global CO2 emissions of 2013, which is significant for global climate change. Secondly, households and individuals in the outer areas of both cities produce higher emissions than those in the inner areas. Thirdly, the lower emissions in Xi'an are due to the higher density and more compact urban pattern, shorter commuting distances, higher transit shares, and more clean energy vehicles. The more dispersed and extensive urban sprawl and the prevalence of twowheeler motorbikes (two-wheeler motorbike is abbreviated as Btwo-wheeler in the following sections) fueled by gasoline cause higher emissions in Bangalore. Fourthly, car availability, higher household income, living outside the 2nd or Outer Ring Road, distance from the bus stop, and working in the foreign companies in Bangalore are significant and positive factors of commuting CO2 emissions. Fifthly, ``70-20'' and ``50-20'' (this means that generally, 20 % of commuters and households produce 70 % of total emissions in Xi'an and 20 % of commuters and households produce 50 % of total emissions in Bangalore) emission patterns exist in Xi'an and Bangalore, respectively. Several strategies have been proposed to reduce urban CO2 emissions produced by commuters and further to mitigate global climate change. Firstly, in the early stage of fast urbanization, enough monetary and land investment should be ensured to develop rail transit or rapid bus routes from outer areas to inner areas in the cities to avoid high dependency on cars, thus to implement the transit-oriented development (TOD), which is the key for Chinese and Indian cities to mitigate the impact on global climate change caused by CO2 emissions. Secondly, in Bangalore, it is necessary to improve public transit service and increase the bus stop coverage combined with car demand controls along the ring roads, in the outer areas, and in the industry areas where Indian foreign companies and the governments are located. Thirdly, Indian should put more efforts to provide alternative cleaner transport modes while China should put more efforts to reduce CO2 emissions from high emitters

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape